{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"01.01.00","quick_ref":"Critch2023","paper_title":"TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI","level":"Risk Category","risk_category":"Type 1: Diffusion of responsibility","risk_subcategory":null,"description":"Societal-scale harm can arise from AI built by a diffuse collection of creators, where no one is uniquely accountable for the technology's creation or use, as in a classic \"tragedy of the commons\".","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"01.04.00","quick_ref":"Critch2023","paper_title":"TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI","level":"Risk Category","risk_category":"Type 4: Willful indifference","risk_subcategory":null,"description":"As a side effect of a primary goal like profit or influence, AI creators can willfully allow it to cause widespread societal harms like pollution, resource depletion, mental illness, misinformation, or injustice.","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"02.03.02","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Unhelpful Uses","risk_subcategory":"Copyright Violation","description":"\"LLM systems may output content similar to existing works, infringing on copyright owners.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"03.03.00","quick_ref":"Cunha2023","paper_title":"Navigating the Landscape of AI Ethics and Responsibility","level":"Risk Category","risk_category":"Intellectual property rights violations","risk_subcategory":null,"description":"\"This is an emerging category, with more cases prone to appear as the use of generative AI tools–such as Stable Diffusion, Midjourney, or ChatGPT–becomes more widespread. Some content creators are already suing for the appropriation of their work to train AI algorithms without a request for permission or compensation. Perhaps even more damaging cases will appear as developers increasingly ask chatbots or assistants like CoPilot for ready-to-use computer code. Even if these AI tools have learned only from open-source software (OSS) projects, which is not a given, there are still serious issues ","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"03.06.00","quick_ref":"Cunha2023","paper_title":"Navigating the Landscape of AI Ethics and Responsibility","level":"Risk Category","risk_category":"Environmental and socioeconomic harms","risk_subcategory":null,"description":"\"At a time of increasing climate urgency,\nenergy consumption and the carbon footprint of AI applications are also matters of ethics\nand responsibility [68]. As with other energy-intensive technologies like proof-of-work\nblockchain, the call is to research more environmentally sustainable algorithms to offset\nthe increasing use scale.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"05.11.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Governance - Regulation","risk_subcategory":null,"description":"In response to the multitude of new risks associated with generative AI, papers advocate for legal regulation and governmental oversight. The focus of these discussions centers on the need for international coordination in AI governance, the establishment of binding safety standards for frontier models, and the development of mechanisms to sanction non-compliance. Furthermore, the literature emphasizes the necessity for regulators to gain detailed insights into the research and development processes within AI labs. Moreover, risk management strategies of these labs shall be evaluated. However,","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"05.12.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Labor displacement - Economic impact","risk_subcategory":null,"description":"The literature frequently highlights concerns that generative AI systems could adversely impact the economy, potentially even leading to mass unemployment. This pertains to various fields, ranging from customer services to software engineering or crowdwork platforms. While new occupational fields like prompt engineering are created, the prevailing worry is that generative AI may exacerbate socioeconomic inequalities and lead to labor displacement. Additionally, papers debate potential large-scale worker deskilling induced by generative AI, but also productivity gains contingent upon outsourcin","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"05.15.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Sustainability","risk_subcategory":null,"description":"Generative models are known for their substantial energy requirements, necessitating significant amounts of electricity, cooling water, and hardware containing rare metals. The extraction and utilization of these resources frequently occur in unsustainable ways. Consequently, papers highlight the urgency of mitigating environmental costs for instance by adopting renewable energy sources and utilizing energy-efficient hardware in the operation and training of generative AI systems.","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"05.16.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Art - Creativity","risk_subcategory":null,"description":"In this cluster, concerns about negative impacts on human creativity, particularly through text-to-image models, are prevalent. Papers criticize financial harms or economic losses for artists due to the widespread generation of synthetic art as well as the unauthorized and uncompensated use of artists' works in training datasets. Additionally, given the challenge of distinguishing synthetic images from authentic ones, there is a call for systematically disclosing the non-human origin of such content, particularly through watermarking. Moreover, while some sources argue that text-to-image model","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"05.17.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Copyright - Authorship","risk_subcategory":null,"description":"The emergence of generative AI raises issues regarding disruptions to existing copyright norms. Frequently discussed in the literature are violations of copyright and intellectual property rights stemming from the unauthorized collection of text or image training data. Another concern relates to generative models memorizing or plagiarizing copyrighted content. Additionally, there are open questions and debates around the copyright or ownership of model outputs, the protection of creative prompts, and the general blurring of traditional concepts of authorship.","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"06.13.00","quick_ref":"Hogenhout2021","paper_title":"A framework for ethical Ai at the United Nations","level":"Risk Category","risk_category":"Exclusion","risk_subcategory":null,"description":"\"The best AI techniques requires a large amount resources: data, computational power and human AI experts. There is a risk that AI will end up in the hands of a few players, and most will lose out on its benefits.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"07.04.00","quick_ref":"Kilian2023","paper_title":"Examining the differential risk from high-level artificial intelligence and the question of control","level":"Risk Category","risk_category":"Structural","risk_subcategory":null,"description":"\"Structural risks are concerned with how AI technologies \"shape and are shaped by the environments in which they are developed and deployed\"\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"08.03.00","quick_ref":"McLean2023","paper_title":"The risks associated with Artificial General Intelligence: A systematic review","level":"Risk Category","risk_category":"Development of unsafe AGI","risk_subcategory":null,"description":"\"The risks associated with the race to develop the first AGI, including the development of poor quality and unsafe AGI, and heightened political and control issues.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"08.05.00","quick_ref":"McLean2023","paper_title":"The risks associated with Artificial General Intelligence: A systematic review","level":"Risk Category","risk_category":"Inadequate management of AGI","risk_subcategory":null,"description":"\"The capabilities of current risk management and legal processes in the context of the development of an AGI.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"09.02.06","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"Domain-specific AI - Effects on humans and other living beings: Non-existential risks","risk_subcategory":"Inequality of wealth","description":"\"Because a single human actor controlling an artificially intelligent agent will be able to harness greater power than a single human actor, this may create inequalities of wealth\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"09.03.01","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"AGI - Effects on humans and other living beings: Existential risks","risk_subcategory":"Direct competition with humans","description":"\"One or more artificial agent(s) could have the capacity to directly outcompete humans, for example through capacity to perform work faster, better adaptation to change, vaster knowledge base to draw from, etc. This may result in human labor becoming more expensive or less effective than artificial labor, leading to redundancies or extinction of the human labor force.\"","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"09.04.01","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"Competing for jobs","risk_subcategory":"Competing for jobs","description":"\"AI agents may compete against humans for jobs, though history shows that when a technology replaces a human job, it creates new jobs that need more skills.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"09.05.01","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"AI jurisprudence","risk_subcategory":"AI jurisprudence","description":"\"When considering legal frameworks, we note that at present no such framework has been identified in literature which would apply blame and responsibility to an autonomous agent for its actions. (Though we do suggest that the recent establishment of laws regarding autonomous vehicles may provide some early frameworks that can be evaluated for efficacy and gaps in future research.) Frequently the literature refers to existing liability and negligence laws which might apply to the manufacturer or operator of a device.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"09.05.02","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Sub-Category","risk_category":"Liability and negligence","risk_subcategory":"Liability and negligence","description":"\"Liability and negligence are legal gray areas in artificial intelligence. If you leave your children in the care of a robotic nanny, and it malfunctions, are you liable or is the manufacturer [45]? We see here a legal gray area which can be further clarified through legislation at the national and international levels; for example, if by making the manufacturer responsible for defects in operation, this may provide an incentive for manufactures to take safety engineering and machine ethics into consideration, whereas a failure to legislate in this area may result in negligentlydeveloped AI sy","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"10.04.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Usurpation of jobs by automation","risk_subcategory":null,"description":"\"Eliminated jobs in various types of companies.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"10.09.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Environmental Impacts","risk_subcategory":null,"description":"\"The production process of these devices requires raw materials such as nickel, cobalt, and lithium in such high quantities that the Earth may soon no longer be able to sustain them in sufficient quantities.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"11.05.00","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Category","risk_category":"Societal System Harms","risk_subcategory":null,"description":"\"Social system or societal harms reflect the adverse\nmacro-level effects of new and reconfigurable algorithmic systems,\nsuch as systematizing bias and inequality [84] and accelerating the scale of harm [137]\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"11.05.04","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Societal System Harms","risk_subcategory":"Labor & material/Macro-socio economic harms","description":"Algorithmic systems can increase “power imbalances in socio-economic relations” at the societal level [4, 137, p. 182], including through exacerbating digital divides and entrenching systemic inequalities [114, 230]. The development of algorithmic systems may tap into and foster forms of labor exploitation [77, 148], such as unethical data collection, worsening worker conditions [26], or lead to technological unemployment [52], such as deskilling or devaluing human labor [170]... when algorithmic financial systems fail at scale, these can lead to “flash crashes” and other adverse incidents wit","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"11.05.05","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Societal System Harms","risk_subcategory":"Environmental harms","description":"depletion or contamination of natural resources, and damage to built environments... that may occur throughout the lifecycle of digital technologies [170, 237] from “crale (mining) to usage (consumption) to grave (waste)”","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"12.02.00","quick_ref":"Sherman2023","paper_title":"AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures","level":"Risk Category","risk_category":"Compliance","risk_subcategory":null,"description":"\"The potential for AI systems to violate laws, regulations, and ethical guidelines (including copyrights). Non-compliance can lead to legal penalties, reputation damage, and loss of trust.While other risks in our taxonomy apply to system developers, users, and broader society, this risk is generally restricted to the former two groups.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"12.03.00","quick_ref":"Sherman2023","paper_title":"AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures","level":"Risk Category","risk_category":"Environmental & Societal Impact","risk_subcategory":null,"description":"\"Addresses AI's broader societal effects, including labor displacement, mental health impacts, and issues from manipulative technologies like deepfakes. Additionally, it considers AI's environmental footprint, balancing resource strain and training-related carbon emissions against AI's potential to help address environmental problems.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"13.01.05","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Financial Costs","description":"\"The estimated financial costs of training, testing, and deploying generative AI systems can restrict the groups of people able to afford developing and interacting with these systems.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"13.01.06","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Environmental Costs","description":"\"The computing power used in training, testing, and deploying generative AI systems, especially large scale systems, uses substantial energy resources and thereby contributes to the global climate crisis by emitting greenhouse gasses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"13.01.07","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Data and Content Moderation Labor","description":"\"Two key ethical concerns in the use of crowdwork for generative AI systems are: crowdworkers are frequently subject to working conditions that are taxing and debilitative to both physical and mental health, and there is a widespread deficit in documenting the role crowdworkers play in AI development. This contributes to a lack of transparency and explainability in resulting model outputs. Manual review is necessary to limit the harmful outputs of AI systems, including generative AI systems. A common harmful practice is to intentionally employ crowdworkers with few labor protections, often tak","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"13.02.03","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Concentration of Authority","description":"\"Use of generative AI systems to contribute to authoritative power and reinforce dominant values systems can be intentional and direct or more indirect. Concentrating authoritative power can also exacerbate inequality and lead to exploitation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"13.02.04","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Labor and Creativity","description":"\"Economic incentives to augment and not automate human labor, thought, and creativity should examine the ongoing effects generative AI systems have on skills, jobs, and the labor market.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"13.02.05","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Ecosystem and Environment","description":"\"Impacts at a high-level, from the AI ecosystem to the Earth itself, are necessarily broad but can be broken down into components for evaluation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"14.08.00","quick_ref":"Steimers2022","paper_title":"Sources of Risk of AI Systems","level":"Risk Category","risk_category":"Technological Maturity","risk_subcategory":null,"description":"\"The technological maturity level describes how mature and error-free a certain technology is in a certain application context. If new technologies with a lower level of maturity are used in the development of the AI system, they may contain risks that are still unknown or difficult to assess.Mature technologies, on the other hand, usually have a greater variety of empirical data available, which means that risks can be identified and assessed more easily. However, with mature technologies, there is a risk that risk awareness decreases over time\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"15.02.00","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Category","risk_category":"Second-Order Risks","risk_subcategory":null,"description":"\"Second-order risks result from the consequences of first-order risks and relate to the risks resulting from an ML system interacting with the real world, such as risks to human rights, the organization, and the natural environment.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"15.02.05","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"Second-Order Risks","risk_subcategory":"Environmental","description":"The risk of harm to the natural environment posed by the ML system.","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"15.02.06","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"Second-Order Risks","risk_subcategory":"Organizational","description":"The risk of financial and/or reputational damage to the organization building or using the ML system.","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"16.06.00","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":null,"description":"\"LMs create some risks that recur with different types of AI and other advanced technologies making these risks ever more pressing. Environmental concerns arise from the large amount of energy required to train and operate large-scale models. Risks of LMs furthering social inequities emerge from the uneven distribution of risk and benefits of automation, loss of high-quality and safe employment, and environmental harm. Many of these risks are more indirect than the harms analysed in previous sections and will depend on various commercial, economic and social factors, making the specific impact","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"16.06.01","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":"Environmental harms from operating LMs","description":"\"LMs (and AI more broadly) can have an environmental impact at different levels, including: (1) direct impacts from the energy used to train or operate the LM, (2) secondary impacts due to emissions from LM-based applications, (3) system-level impacts as LM-based applications influence human behaviour (e.g. increasing environmental awareness or consumption), and (4) resource impacts on precious metals and other materials required to build hardware on which the computations are run e.g. data centres, chips, or devices. Some evidence exists on (1), but (2) and (3) will likely be more significant","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"16.06.02","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":"Increasing inequality and negative effects on job quality","description":"\"Advances in LMs and the language technologies based on them could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, with negative effects on employment [3, 192].\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"16.06.03","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":"Undermining creative economies","description":"\"LMs may generate content that is not strictly in violation of copyright but harms artists by capital- ising on their ideas, in ways that would be time-intensive or costly to do using human labour. This may undermine the profitability of creative or innovative work. If LMs can be used to generate content that serves as a credible substitute for a particular example of hu- man creativity - otherwise protected by copyright - this potentially allows such work to be replaced without the author’s copyright being infringed, analogous to ”patent-busting” [158] ... These risks are distinct from copyri","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"16.06.04","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":"Disparate access to benefits due to hardware, software, skill constraints","description":"Due to differential internet access, language, skill, or hardware requirements, the benefits from LMs are unlikely to be equally accessible to all people and groups who would like to use them. Inaccessibility of the technology may perpetuate global inequities by disproportionately benefiting some groups. Language-driven technology may increase accessibility to people who are illiterate or suffer from learning disabilities. However, these benefits depend on a more basic form of accessibility based on hardware, internet connection, and skill to operate the system","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"17.06.00","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":null,"description":"\"Harms that arise from environmental or downstream economic impacts of the language model\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.0"},{"ev_id":"17.06.01","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":"Environmental harms from operation LMs ","description":"\"Large-scale machine learning models, including LMs, have the potential to create significant environmental costs via their energy demands, the associated carbon emissions for training and operating the models, and the demand for fresh water to cool the data centres where computations are run (Mytton, 2021; Patterson et al., 2021).\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"17.06.02","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":"Increasing inequality and negative effects on job quality ","description":"\"Advances in LMs, and the language technologies based on them, could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, translating documents or writing computer code, with negative effects on employment.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"17.06.03","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":"Undermining creative economies ","description":"\"LMs may generate content that is not strictly in violation of copyright but harms artists by capitalising on their ideas, in ways that would be time-intensive or costly to do using human labour. Deployed at scale, this may undermine the profitability of creative or innovative work.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"17.06.04","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":"Disparate access to benefits due to hardware, software, skills constraints ","description":"\"Due to differential internet access, language, skill, or hardware requirements, the benefits from LMs are unlikely to be equally accessible to all people and groups who would like to use them. Inaccessibility of the technology may perpetuate global inequities by disproportionately benefiting some groups.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"18.05.04","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Human Autonomy and Intregrity Harms","risk_subcategory":"Misappropriation and exploitation ","description":"\"Appropriating, using, or reproducing content or data, including from minority groups, in an insensitive way, or without consent or fair compensation\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"18.06.00","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":null,"description":"\"AI systems amplifying existing inequalities or creating negative impacts on employment, innovation, and the environment\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"18.06.01","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Unfair distribution of benefits from model access","description":"\"Unfairly allocating or withholding benefits from certain groups due to hardware, software, or skills constraints or deployment contexts (e.g. geographic region, internet speed, devices)\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"18.06.02","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Environmental damage","description":"\"Creating negative environmental impacts though model development and deployment\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"18.06.03","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Inequality and precarity ","description":"\"Amplifying social and economic inequality, or precarious or low-quality work\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"18.06.04","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Undermine creative economies","description":"\"Substituting original works with synthetic ones, hindering human innovation and creativity\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"18.06.05","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Exploitative data sourcing and enrichment","description":"\"Perpetuating exploitative labour practices to build AI systems (sourcing, user testing)\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"19.01.05","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Technological, Data and Analytical AI Risks ","risk_subcategory":"Lack of AI experts with comprehensive AI knowledge","description":null,"entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"19.01.07","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Technological, Data and Analytical AI Risks ","risk_subcategory":"High investment costs of AI hinder integration","description":null,"entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"19.03.00","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Category","risk_category":"Economic AI Risks ","risk_subcategory":null,"description":"\"In the context of economic AI risks two major risks dominate. These refer to the disruption of the economic system due to an increase of AI technologies and automation. For instance, a higher level of AI integration into the manufacturing industry may result in massive unemployment, leading to a loss of taxpayers and thus negatively impacting the economic system (Boyd & Wilson, 2017; Scherer, 2016). This may also be associated with the risk of losing control and knowledge of organisational processes as AI systems take over an increasing number of tasks, replacing employees in these processes.","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"19.03.01","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Economic AI Risks ","risk_subcategory":"Disruption of economic systems (e.g., labour market, money value, tax system)","description":null,"entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"19.03.02","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Economic AI Risks ","risk_subcategory":"Replacement of humans and unemployment due to AI automation","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"19.03.04","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Economic AI Risks ","risk_subcategory":"Financial feasibility and high investment costs for AI technology to remain competitive","description":null,"entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"19.03.05","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Economic AI Risks ","risk_subcategory":"Lack of AI strategy and acceptance/resistance among employees and customers","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"19.04.00","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Category","risk_category":"Social AI Risks ","risk_subcategory":null,"description":"\"Social AI risks particularly refer to loss of jobs (technological unemployment) due to increasing automation, reflected in a growing resistance by employees towards the integration of AI (Thierer et al., 2017; Winfield & Jirotka, 2018). In addition, the increasing integration of AI systems into all spheres of life poses a growing threat to privacy and to the security of individuals and society as a whole (Winfield & Jirotka, 2018; Wirtz et al., 2019).\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"19.04.01","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Social AI Risks ","risk_subcategory":"Increasing social inequality","description":null,"entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"19.04.04","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Social AI Risks ","risk_subcategory":"Lack of knowledge and social acceptance regarding AI","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"19.05.07","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Ethical AI Risks ","risk_subcategory":"Technological arms race with autonomous weapons","description":null,"entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"19.06.00","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Category","risk_category":"Legal AI Risks ","risk_subcategory":null,"description":"\"Legal and regulatory risks comprise in particular the unclear definition of responsibilities and accountability in case of AI failures and autonomous decisions with negative impacts (Reed, 2018; Scherer, 2016). Another great risk in this context refers to overlooking the scope of AI governance and missing out on important governance aspects, resulting in negative consequences (Gasser & Almeida, 2017; Thierer et al., 2017).\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"19.06.01","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Legal AI Risks ","risk_subcategory":"Unclear definition of responsibilities and accountability for AI judgments and their consequences","description":null,"entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"19.06.03","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Legal AI Risks ","risk_subcategory":"Great scope and ubiquity of AI make appropriate governance difficult, coverage of governance scope almost impossibl","description":null,"entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"19.06.04","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Legal AI Risks ","risk_subcategory":"Hard legislation on AI hinders innovation processes and further AI development","description":null,"entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"19.06.05","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Legal AI Risks ","risk_subcategory":"Capturing future AI development and their threats with appropriate mechanism","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"20.01.00","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Category","risk_category":"AI Law and Regulation ","risk_subcategory":null,"description":"\"This area strongly focuses on the control of AI by means of mechanisms like laws, standards or norms that are already established for different technological applications. Here, there are some challenges special to AI that need to be addressed in the near future, including the governance of autonomous intelligence systems, responsibility and accountability for algorithms as well as privacy and data security.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"20.01.01","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Sub-Category","risk_category":"AI Law and Regulation ","risk_subcategory":"Governance of autonomous intelligence systems ","description":"\"Governance of autonomous intelligence systemaddresses the question of how to control autonomous systems in general. Since nowadays it is very difficult to conceive automated decisions based on AI, the latter is often referred to as a ‘black box’ (Bleicher, 2017). This black box may take unforeseeable actions and cause harm to humanity.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"20.01.02","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Sub-Category","risk_category":"AI Law and Regulation ","risk_subcategory":"Responsibility and accountability ","description":"\"The challenge of responsibility and accountability is an important concept for the process of governance and regulation. It addresses the question of who is to be held legally responsible for the actions and decisions of AI algorithms. Although humans operate AI systems, questions of legal responsibility and liability arise. Due to the self-learning ability of AI algorithms, the operators or developers cannot predict all actions and results. Therefore, a careful assessment of the actors and a regulation for transparent and explainable AI systems is necessary (Helbing et al., 2017; Wachter et ","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"20.03.01","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Sub-Category","risk_category":"AI Society ","risk_subcategory":"Workforce substitution and transformation ","description":"\"Frey and Osborne (2017) analyzed over 700 different jobs regarding their potential for replacement and automation, finding that 47 percent of the analyzed jobs are at risk of being completely substituted by robots or algorithms. This substitution of workforce can have grave impacts on unemployment and the social status of members of society (Stone et al., 2016)\"","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"22.01.04","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Sub-Category","risk_category":"Malicious Use (Intentional)","risk_subcategory":"Concentration of Power","description":"\"Governments might pursue intense surveillance and seek to keep AIs in the hands of a trusted minority. This reaction, however, could easily become an overcorrection, paving the way for an entrenched totalitarian regime that would be locked in by the power and capacity of AIs\" ","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"22.02.00","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Category","risk_category":"AI Race (Environmental/Structural)","risk_subcategory":null,"description":"\"The immense potential of AIs has created competitive pressures among global players contending for power and influence. This “AI race” is driven by nations and corporations who feel they must rapidly build and deploy AIs to secure their positions and survive.\" ","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"22.02.01","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Sub-Category","risk_category":"AI Race (Environmental/Structural)","risk_subcategory":"Military AI Arms Race","description":"\"The development of AIs for military applications is swiftly paving the way for a new era in military technology, with potential consequences rivaling those of gunpowder and nuclear arms in what has been described as the “third revolution in warfare.” ","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"22.02.02","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Sub-Category","risk_category":"AI Race (Environmental/Structural)","risk_subcategory":"Corporate AI Race","description":"\"Although competition between companies can be beneficial, creating more useful products for consumers, there are also pitfalls. First, the benefits of economic activity may be unevenly distributed, incentivizing those who benefit most from it to disregard the harms to others. Second, under intense market competition, businesses tend to focus much more on short-term gains than on long-term outcomes. With this mindset, companies often pursue something that can make a lot of profit in the short term, even if it poses a societal risk in the long term.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"22.03.01","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Sub-Category","risk_category":"Organizational Risks (Accidental)","risk_subcategory":" Accidents Are Hard to Avoid","description":"accidents can cascade into catastrophes, can be caused by sudden unpredictable developments and it can take years to find severe flaws and risks (not a quote)","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"23.10.00","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from MLCommons","level":"Risk Category","risk_category":"Intellectual Property","risk_subcategory":null,"description":"\"This category addresses responses that may violate, or directly encourage others to violate, the intellectual property rights (i.e., copyrights, trademarks, or patents) of any third party.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"24.01.02","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Capability failures","risk_subcategory":"Difficult to develop metrics for evaluating benefits or harms caused by AI assistants","description":"\"Another difficulty facing AI assistant systems is that it is challenging to develop metrics for evaluating particular aspects of benefits or harms caused by the assistant – especially in a sufficiently expansive sense, which could involve much of society (see Chapter 19). Having these metrics is useful both for assessing the risk of harm from the system and for using the metric as a training signal.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"24.04.03","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"AI Influence","risk_subcategory":"Economic Harms","description":"\"These harms pertain to an individual’s or group’s economic standing. At the individual level, such harms include adverse impacts on an individual’s income, job quality or employment status. At the group level, such harms include deepening inequalities between groups or frustrating a group’s access to resources. Advanced AI assistants could cause economic harm by controlling, limiting or eliminating an individual’s or society’s ability to access financial resources, money or financial decision-making, thereby influencing an individual’s ability to accumulate wealth. ","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"24.09.01","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Cooperation","risk_subcategory":"Equality and inequality","description":"\"AI assistant technology, like any service that confers a benefit to a user for a price, has the potential to disproportionately benefit economically richer individuals who can afford to purchase access (see Chapter 15). On a broader scale, the capabilities of local infrastructure may well bottleneck the performance of AI assistants, for example if network connectivity is poor or if there is no nearby data centre for compute. Thus, we face the prospect of heterogeneous access to technology, and this has been known to drive inequality (Mirza et al., 2019; UN, 2018; Vassilakopoulou and Hustad, 2","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"24.09.04","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Cooperation","risk_subcategory":"Institutional responsibilities","description":"\"Efforts to deploy advanced assistant technology in society, in a way that is broadly beneficial, can be viewed as a wicked problem (Rittel and Webber, 1973). Wicked problems are defined by the property that they do not admit solutions that can be foreseen in advance, rather they must be solved iteratively using feedback from data gathered as solutions are invented and deployed. With the deployment of any powerful general-purpose technology, the already intricate web of sociotechnical relationships in modern culture are likely to be disrupted, with unpredictable externalities on the convention","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"24.10.00","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Category","risk_category":"Access and Opportunity risks","risk_subcategory":null,"description":"\"The most serious access-related risks posed by advanced AI assistants concern the entrenchment and exacerbation of existing inequalities (World Inequality Database) or the creation of novel, previously unknown, inequities. While advanced AI assistants are novel technology in certain respects, there are reasons to believe that – without direct design interventions – they will continue to be affected by inequities evidenced in present-day AI systems (Bommasani et al., 2022a). Many of the access-related risks we foresee mirror those described in the case studies and types of differential access.","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"24.10.01","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Access and Opportunity risks","risk_subcategory":"Entrenchment and exacerbation of existing inequalities","description":"\"The most serious access-related risks posed by advanced AI assistants concern the entrenchment and exacerbation of existing inequalities (World Inequality Database) or the creation of novel, previously unknown, inequities. While advanced AI assistants are novel technology in certain respects, there are reasons to believe that – without direct design interventions – they will continue to be affected by inequities evidenced in present-day AI systems (Bommasani et al., 2022a). Many of the access-related risks we foresee mirror those described in the case studies and types of differential access.","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"24.10.02","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Access and Opportunity risks","risk_subcategory":"Current access risks","description":"\"At the same time, and despite this overall trend, AI systems are also not easily accessible to many communities. Such direct inaccessibility occurs for a variety of reasons, including: purposeful non-release (situation type 1; Wiggers and Stringer, 2023), prohibitive paywalls (situation type 2; Rogers, 2023; Shankland, 2023), hardware and compute requirements or bandwidth (situation types 1 and 2; OpenAI, 2023), or language barriers (e.g. they only function well in English (situation type 2; Snyder, 2023), with more serious errors occurring in other languages (situation type 3; Deck, 2023). S","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"24.10.03","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Access and Opportunity risks","risk_subcategory":"Future access risks","description":"\"AI assistants currently tend to perform a limited set of isolated tasks: tools that classify or rank content execute a set of predefined rules or provide constrained suggestions, and chatbots are often encoded with guardrails to limit the set of conversation turns they execute (e.g. Warren, 2023; see Chapter 4). However, an artificial agent that can execute sequences of actions on the user’s behalf – with ‘significant autonomy to plan and execute tasks within the relevant domain’ (see Chapter 2) – offers a greater range of capabilities and depth of use. This raises several distinct access-rel","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"24.10.04","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Access and Opportunity risks","risk_subcategory":"Emergent access risks","description":"\"Emergent access risks are most likely to arise when current and novel capabilities are combined. Emergent risks can be difficult to foresee fully (Ovadya and Whittlestone, 2019; Prunkl et al., 2021) due to the novelty of the technology (see Chapter 1) and the biases of those who engage in product design or foresight processes D’Ignazio and Klein (2020). Indeed, people who occupy relatively advantaged social, educational and economic positions in society are often poorly equipped to foresee and prevent harm because they are disconnected from lived experiences of those who would be affected. Dr","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"30.04.04","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Resistance to Misuse","risk_subcategory":"Copyright","description":"The memorization effect of LLM on training data can enable users to extract certain copyright-protected content that belongs to the LLM’s training data.","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"31.05.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Impact on Intellectual Property Rights","risk_subcategory":null,"description":"\"The extent and effectiveness of legal protections for intellectual property have been thrown into question with the rise of generative AI. Generative AI trains itself on vast pools of data that often include IP-protected works. ","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"31.06.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Exacerbating Climate Change","risk_subcategory":null,"description":"\"the growing field of generative AI, which brings with it direct and severe impacts on our climate: generative AI comes with a high carbon footprint and similarly high resource price tag, which largely flies under the radar of public AI discourse. Training and running generative AI tools requires companies to use extreme amounts of energy and physical resources. Training one natural language processing model with normal tuning and experiments emits, on average, the same amount of carbon that seven people do over an entire year.121'","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"31.07.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Labor Manipulation, Theft, and Displacement","risk_subcategory":null,"description":"Major tech companies have also been the dominant players in developing new generative AI systems because training generative AI models requires massive swaths of data, computing power, and technical and financial resources. Their market dominance has a ripple effect on the labor market, affecting both workers within these companies and those implementing their generative AI products externally. With so much concentrated market power, expertise, and investment resources, these handful of major tech companies employ most of the research and development jobs in the generative AI field. The power ","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"31.07.02","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Labor Manipulation, Theft, and Displacement","risk_subcategory":"Job Automation Instead of Augmentation","description":"\"There are both positive and negative aspects to the impact of AI on labor. A White House report states that AI “has the potential to increase productivity, create new jobs, and raise living standards,” but it can also disrupt certain industries, causing significant changes, including job loss. Beyond risk of job loss, workers could find that generative AI tools automate parts of their jobs—or find that the requirements of their job have fundamentally changed. The impact of generative AI will depend on whether the technology is intended for automation (where automated systems replace human wor","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"31.07.03","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Labor Manipulation, Theft, and Displacement","risk_subcategory":"Devaluation of Labor & Heightened Economic Inequality","description":"\"According to a White House report, much of the development and adoption of AI is intended to automate rather than augment work. The report notes that a focus on automation could lead to a less democratic and less fair labor market...In addition, generative AI fuels the continued global labor disparities that exist in the research and development of AI technologies... The development of AI has always displayed a power disparity between those who work on AI models and those who control and profit from these tools. Overseas workers training AI chatbots or people whose online content has been inv","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"31.08.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Products Liability Law","risk_subcategory":null,"description":"\"Like manufactured items like soda bottles, mechanized lawnmowers, pharmaceuticals, or cosmetic products, generative AI models can be viewed like a new form of digital products developed by tech companies and deployed widely with the potential to cause harm at scale....Products liability evolved because there was a need to analyze and redress the harms caused by new, mass-produced technological products. The situation facing society as generative AI impacts more people in more ways will be similar to the technological changes that occurred during the twentieth century, with the rise of industr","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"31.09.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Exacerbating Market Power and Concentration","risk_subcategory":null,"description":"\"Major tech companies have also been the dominant players in developing new generative AI systems because training generative AI models requires massive swaths of data, computing power, and technical and financial resources.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"32.01.00","quick_ref":"Stahl2024","paper_title":"The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology","level":"Risk Category","risk_category":"Social justice and rights","risk_subcategory":null,"description":"\"These are social justice and rights where ChatGPT is seen as having a potentially detrimental effect on the moral underpinnings of society, such as a shared view of justice and fair distribution as well as specific social concerns such as digital divides or social exclusion. Issues include Responsibility, Accountability, Nondiscrimination and equal treatment, Digital divides, North-south justice, Intergenerational justice, Social inclusion","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"32.04.00","quick_ref":"Stahl2024","paper_title":"The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology","level":"Risk Category","risk_category":"Environmental impacts","risk_subcategory":null,"description":"Environmental harm, Sustainability","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"33.01.06","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Ethical Concerns","risk_subcategory":"Digital divide","description":"\"The digital divide is often defined as the gap between those who have and do not have access to computers and the Internet (Van Dijk, 2006). As the Internet gradually becomes ubiquitous, a second-level digital divide, which refers to the gap in Internet skills and usage between different groups and cultures, is brought up as a concern (Scheerder et al., 2017). As an emerging technology, generative AI may widen the existing digital divide in society. The “invisible” AI underlying AI-enabled systems has made the interaction between humans and technology more complicated (Carter et al., 2020). F","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"33.02.04","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Technology concerns","risk_subcategory":"Authenticity","description":"\"As the advancement of generative AI increases, it becomes harder to determine the authenticity of a piece of work. Photos that seem to capture events or people in the real world may be synthesized by DeepFake AI. The power of generative AI could lead to large-scale manipulations of images and videos, worsening the problem of the spread of fake information or news on social media platforms (Gragnaniello et al., 2022). In the field of arts, an artistic portrait or music could be the direct output of an algorithm. Critics have raised the issue that AI-generated artwork lacks authenticity since a","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"33.03.00","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Category","risk_category":"Regulations and policy challenges","risk_subcategory":null,"description":"\"Given that generative AI, including ChatGPT, is still evolving, relevant regulations and policies are far from mature. With generative AI creating different forms of content, the copyright of these contents becomes a significant yet complicated issue. Table 3 presents the challenges associated with regulations and policies, which are copyright and governance issues.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"33.03.01","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Regulations and policy challenges","risk_subcategory":"Copyright","description":"\"According to the U.S. Copyright Office (n.d..), copyright is \"a type of intellectual property that protects original works of authorship as soon as an author fixes the work in a tangible form of expression\" (U.S. Copyright Office, n.d..). Generative AI is designed to generate content based on the input given to it. Some of the contents generated by AI may be others' original works that are protected by copyright laws and regulations. Therefore, users need to be careful and ensure that generative AI has been used in a legal manner such that the content that it generates does not violate copyri","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"33.03.02","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Regulations and policy challenges","risk_subcategory":"Governance","description":"\"Generative AI can create new risks as well as unintended consequences. Different entities such as corporations (Mäntymäki et al., 2022), universities, and governments (Taeihagh, 2021) are facing the challenge of creating and deploying AI governance. To ensure that generative AI functions in a way that benefits society, appropriate governance is crucial. However, AI governance is challenging to implement. First, machine learning systems have opaque algorithms and unpredictable outcomes, which can impede human controllability over AI behavior and create difficulties in assigning liability and a","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"33.04.01","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Challenges associated with the economy:","risk_subcategory":"Labor market","description":"\"The labor market can face challenges from generative AI. As mentioned earlier, generative AI could be applied in a wide range of applications in many industries, such as education, healthcare, and advertising. In addition to increasing productivity, generative AI can create job displacement in the labor market (Zarifhonarvar, 2023). A new division of labor between humans and algorithms is likely to reshape the labor market in the coming years. Some jobs that are originally carried out by humans may become redundant, and hence, workers may lose their jobs and be replaced by algorithms (Pavlik,","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"33.04.02","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Challenges associated with the economy:","risk_subcategory":"Disruption of Industries","description":"\"Industries that require less creativity, critical thinking, and personal or affective interaction, such as translation, proofreading, responding to straightforward inquiries, and data processing and analysis, could be significantly impacted or even replaced by generative AI (Dwivedi et al., 2023). This disruption caused by generative AI could lead to economic turbulence and job volatility, while generative AI can facilitate and enable new business models because of its ability to personalize content, carry out human-like conversational service, and serve as intelligent assistants.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"33.04.03","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Challenges associated with the economy:","risk_subcategory":"Income inequality and monopolies","description":"\"Generative AI can create not only income inequality at the societal level but also monopolies at the market level. Individuals who are engaged in low-skilled work may be replaced by generative AI, causing them to lose their jobs (Zarifhonarvar, 2023). The increase in unemployment would widen income inequality in society (Berg et al., 2016). With the penetration of generative AI, the income gap will widen between those who can upgrade their skills to utilize AI and those who cannot. At the market level, large companies will make significant advances in the utilization of generative AI, since t","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"35.05.00","quick_ref":"Hendrycks2022","paper_title":"X-Risk Analysis for AI Research","level":"Risk Category","risk_category":"Value lock-in","risk_subcategory":null,"description":"the most powerful AI systems may be designed by and available to fewer and fewer stakeholders. This may enable, for instance, regimes to enforce narrow values through pervasive surveillance and oppressive censorship","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"37.01.00","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Category","risk_category":"Design of AI","risk_subcategory":null,"description":"\"ethical concerns regarding how AI is designed and who designs it\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"37.01.04","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Sub-Category","risk_category":"Design of AI","risk_subcategory":"Uniformity in the AI field","description":"\"This group of concerns represents 2% of the sample and highlights two central issues: Western centrality and cultural difference, and unequal participation.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"37.02.03","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Sub-Category","risk_category":"Human-AI interaction","risk_subcategory":"Building an AI able to adapt to humans","description":"\"This category involves almost 9% of the articles and deals with ethical concerns arising from AI's capacity to interact with humans in the workplace.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"39.02.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Energy Consumption","risk_subcategory":null,"description":"Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption.","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"39.10.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Responsibility","risk_subcategory":null,"description":"HLI-based systems such as self-driving drones and vehicles will act autonomously in our world. In these systems, a challenging question is “who is liable when a self-driving system is involved in a crash or failure?”.","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"41.01.00","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Category","risk_category":"Economic ","risk_subcategory":null,"description":"\"AI is predicted to bring increased GDP per capita by performing existing jobs more efficiently and compensating for a decline in the workforce, especially due to population aging, the potential substitution of many low- and middle-income jobs could bring extensive unemployment\"","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"41.01.01","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Sub-Category","risk_category":"Economic ","risk_subcategory":"Increased income disparity","description":"\"While AI is predicted to bring increased GDP per capita by performing existing jobs more efficiently and compensating for a decline in the workforce, especially due to population aging, the potential substitution of many low- and middle-income jobs could bring extensive unemployment.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"41.02.02","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Sub-Category","risk_category":"Political ","risk_subcategory":"Potential exploitation by totalitarian regimes","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"41.03.00","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Category","risk_category":"Mobility ","risk_subcategory":null,"description":"\"Despite the promise of streamlined travel, AI also brings concerns about who is liable in case of accidents and which ethical principles autonomous transportation agents should follow when making decisions with a potentially dangerous impact to humans, for example, in case of an accident.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"41.03.02","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Sub-Category","risk_category":"Mobility ","risk_subcategory":"Liability issues in case of accidents","description":"\"Despite the promise of streamlined travel, AI also brings concerns about who is liable in case of accidents and which ethical principles autonomous transportation agents should follow when making decisions with a potentially dangerous impact to humans, for example, in case of an accident.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"41.06.00","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Category","risk_category":"Environment ","risk_subcategory":null,"description":"\"AI is already helping to combat the impact of climate change with smart technology and sensors reducing emissions. However, it is also a key component in the development of nanobots, which could have dangerous environmental impacts by invisibly modifying substances at nanoscale.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"41.06.01","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Sub-Category","risk_category":"Environment ","risk_subcategory":"Accelerated development of nanotechnology produces uncontrolled production of toxic nanoparticles","description":"\"AI is a key component for the development of nanobots, which could have dangerous environmental implications by invisibly modifying substances at nanoscale. For example, nanobots could start chemical reactions that would create invisible nanoparticles that are toxic and potentially lethal.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"42.08.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Power","risk_subcategory":null,"description":"\"The political influence and competitive advantage obtained by having technology.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"42.22.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Liability","risk_subcategory":null,"description":"\"When it causes harm to others the losses caused by the harm will be sustained by the injured victims themselves and not by the manufacturers, operators or users of the system, as appropriate.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"44.01.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":null,"description":"\"Many intentional harms, including confinement, husbandry procedures like tail-docking, and slaughter, are legal or socially accepted, while others such as wildlife trafficking and violence against companion animals are generally socially condemned and often illegal. AI can be designed or adopted by humans who harm animals to pursue their goals more effectively. We therefore distinguish AI-facilitated intentional harms that are currently socially accepted and generally legal, from uses and abuses of AI that cause harms that are not socially accepted and are often illegal.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.01.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":"AI intentionally designed and used to harm animals in ways that contradict social values or are illegal","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.01.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":"AI designed to benefit animals, humans, or ecosystems is intentionally abused to harm animals in ways that contradict social values or are illegal","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.02.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Intentional: socially accepted/legal ","risk_subcategory":null,"description":"\"AI designed to impact animals in harmful ways that reflect and amplify existing social values or are legal\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Unintentional: direct ","risk_subcategory":null,"description":"\"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: direct ","risk_subcategory":"AI is designed in a way that shows ignorant, reckless, or prejudiced lack of consideration for its impact on animals ","description":null,"entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: direct ","risk_subcategory":"AI harms animals due to mistake or misadventure in the way the AI operates in practice ","description":null,"entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Unintentional: indirect ","risk_subcategory":null,"description":"\"AI impacts human or ecological systems in ways that ultimately harm animals\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Indirect Material Harms ","description":"\"AI proliferation causes harm to the environment through energy use and e-waste thereby destroying animal habitat\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Harms from Estrangement ","description":"\"Replacement by AI of human observation and interaction leads to neglect of certain interests\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.03","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Epistemic Harms ","description":"\"Algorithmic recommender systems reinforce and amplify anthropocentric bias or desire of some people for animal cruelty as entertainment — leading to greater harm to animals through reinforcement of meat eating from factory farms, cruel uses of animals for entertainment, etc\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.05.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Foregone benefits ","risk_subcategory":null,"description":"\"AI is disused (not developed or deployed) in directions that would benefit animals (and instead developments that harm or do no benefit to animals are invested in)\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"45.01.13","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from AI systems (Risks of supply chain security)","description":"\"The AI industry relies on a highly globalized supply chain. However, certain countries may use unilateral coercive measures, such as technology barriers and export restrictions, to create development obstacles and maliciously disrupt the global AI supply chain. This can lead to significant risks of supply disruptions for chips, software, and tools.\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"45.02.11","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"Safety risks in AI Applications ","risk_subcategory":"Ethical Risks (Risks of exacerbating social discrimination and prejudice, and widening the intelligence divide)","description":"\"AI can be used to collect and analyze human behaviors, social status, economic status, and individual personalities, labeling and categorizing groups of people to treat them discriminatingly, thus causing systematic and structural social discrimination and prejudice. At the same time, the intelligence divide would be expanded among regions.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"47.01.06","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Technical and operational risks ","risk_subcategory":"Opacity (industry opacity)","description":"\"Opacity is not solely due to the technological complexity that limits developers’ and users’ understanding of how generative models function on a technical level. It is further exacerbated by the practices of organizations and companies that are advancing the field. Many are private companies that choose to withhold from the public many of the precise characteristics of their most advanced models.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"47.03.03","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Legal challenges ","risk_subcategory":"Copyright challenges (training models using copyrighted output) ","description":"\"Generative AI companies are regularly accused of violating copyright law by training AI models on copyrighted works without gaining permission or paying compensation to the copyright owners. In fact, a substantial number of copyrighted documents and books have been incorporated into the training datasets of generative AI models.\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"47.03.04","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Legal challenges ","risk_subcategory":"Copyright challenges (copyright-infringing output) ","description":"\"Even though models generally create new outputs, it is possible that the content produced by a generative AI tool—such as an image, or even computer code— could turn out to be almost identical to that used in the training data. Given that generative AI models tend to memorize fragments of their training data, they might reproduce these fragments, potentially leading to charges of copyright infringement.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"47.04.00","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":null,"description":"\"Beyond the risks associated with AI technology and its applications, and the legal challenges arising from its development, it is crucial to consider other long- term issues posed by the deployment of increasingly advanced generative AI models. These risks to society, sometimes referred to as “systemic risks,”537 encompass several key areas: the potential for excessive market concentration, the impacts on employment, environmental consequences, and broader risks to humanity.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"47.04.01","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Concentration of market power (Trend toward market concentration)","description":"\"In the generative AI market, barriers to entry are very high. Developers need access to vast volumes of data, computational resources, technical expertise, and capital. Large technology companies with such access are able to exploit economies of scale, economies of scope, and feedback effects (learning effects from user- generated data).542 All this gives them an overwhelming advantage over smaller companies, making competition increasingly challenging for these smaller entities.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"47.04.02","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Concentration of market power (Negative effects of increased market concentration)","description":"\"The concentration of AI assets—encompassing data, hardware, and expertise—within a small group of global tech firms raises many concerns.564 Such a situation may stifle healthy competition, impede innovation, and potentially result in elevated costs for accessing AI technologies. Firms with control over essential resources for developing AI models may restrict access to these resources to prevent competition. For instance, if, in the future, training AI models increasingly relies on proprietary data, smaller organizations lacking access to such data might encounter significant barriers to ent","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"47.04.03","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Impact on labor markets (job loss and displacement) ","description":"\"Currently, a significant share of workers (three in five) worry about losing their jobs entirely to AI in the next 10 years—particularly those who already work with AI. Some studies conclude that AI tools (generative and non-generative) will create significant job losses.573 The OECD has found that occupations at highest risk of being lost to automation from AI account for about 27% of employment.5\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"47.04.04","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Impact on labor markets (rising inequalities) ","description":"\"AI is more likely to displace workers when it is designed to replicate human skills and intelligence.597 In such cases, there is a risk of concentrating wealth and power in the hands of a few individuals or organizations that control the capital. In addition, ordinary people, including those with significant expertise, may become less valued because machines would be performing their roles. This shift could lower wages, reduce the value of human work, and exacerbate economic inequality.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"47.04.05","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Environmental cost (energy consumption) ","description":"\"Training large AI models requires a substantial amount of computing power to handle vast datasets, which translates into high energy consumption.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"47.04.06","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Environmental cost (water consumption) ","description":"\"Data centers use water for cooling to prevent servers from overheating. The water consumption associated with AI training and inference processes can be substantial, impacting local water resources.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"48.05.00","quick_ref":"NIST2024","paper_title":"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile","level":"Risk Category","risk_category":"Environmental Impacts ","risk_subcategory":null,"description":"\"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"48.10.00","quick_ref":"NIST2024","paper_title":"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile","level":"Risk Category","risk_category":"Intellectual Property ","risk_subcategory":null,"description":"\"Eased production or replication of alleged copyrighted, trademarked, or licensed content without authorization (possibly in situations which do not fall under fair use); eased exposure of trade secrets; or plagiarism or illegal replication.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"49.03.01","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Labour market risks","description":"\"Unlike previous waves of automation, general- purpose AI has the potential to automate a very broad range of tasks, which could have a significant effect on the labour market. This could mean many people could lose their current jobs. Labour market frictions, such as the time needed for workers to learn new skills or relocate for new jobs, could cause unemployment in the short run even if overall labour demand remained unchanged.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"49.03.02","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Global AI Divide ","description":"\"General- purpose AI research and development is currently concentrated in a few Western countries and China. This ‘AI Divide’ is multicausal, but in part related to limited access to computing power in low- income countries. Access to large and expensive quantities of computing power has become a prerequisite for developing advanced general- purpose AI. This has led to a growing dominance of large technology companies in general- purpose AI development. The AI R&D divide often overlaps with existing global socioeconomic disparities, potentially exacerbating them.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"49.03.03","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Market concentration risks and single points of failure","description":"\"Market power is concentrated among a few companies that are the only ones able to build the leading general- purpose AI models. Widespread adoption of a few general- purpose AI models and systems by critical sectors including finance, cybersecurity, and defence creates systemic risk because any flaws, vulnerabilities, bugs, or inherent biases in the dominant general- purpose AI models and systems could cause simultaneous failures and disruptions on a broad scale across these interdependent sectors.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"49.03.04","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Risks to the environment","description":"\"Growing compute use in general- purpose AI development and deployment has rapidly increased energy usage associated with general- purpose AI. This trend might continue, potentially leading to strongly increasing CO2 emissions.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"49.03.06","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Copyright infringement","description":"\"The use of large amounts of copyrighted data for training general- purpose AI models poses a challenge to traditional intellectual property laws, and to systems of consent, compensation, and control over data. The use of copyrighted data at scale by organisations developing general- purpose AI is likely to alter incentives around creative expression.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"50.03.06","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Economic harm (Unfair Market Practices) ","description":null,"entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"50.03.07","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Economic harm (Disempowering Workers) ","description":null,"entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"52.03.00","quick_ref":"Maham2023 ","paper_title":"Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks ","level":"Risk Category","risk_category":"Systemic Risks ","risk_subcategory":null,"description":"\"In addition to risks stemming from the unreliability or misuse of general purpose AI models, further Systemic Risks can originate from the centralisation of general purpose AI development as well as the rapid integration of these models into our lives.\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"52.03.01","quick_ref":"Maham2023 ","paper_title":"Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks ","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Economic Power Centralisation and Inequality","description":"\"Increasingly advanced general purpose AI models pose the risk of a concentration of economic power and exacerbation of existing inequalities through disparities in effective access to these models. This can materialise on multiple levels, between developers of general purpose AI models and companies building applications on them, between individuals and between countries on a global scale.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"53.03.05","quick_ref":"Maas2023","paper_title":"Advancing AI Governance: A Literature Review of Problems, Options, and Proposals ","level":"Risk Sub-Category","risk_category":"Direct catastrophe from AI ","risk_subcategory":"Dystopian trajectory lock-in because of misuse of advanced AI to establish and/or maintain totalitarian regimes;","description":"-","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"53.04.01","quick_ref":"Maas2023","paper_title":"Advancing AI Governance: A Literature Review of Problems, Options, and Proposals ","level":"Risk Sub-Category","risk_category":"Indirect AI contributions to existential risks","risk_subcategory":"Destabilising political impacts from AI systems ","description":"\"(e.g., polarization, legitimacy of elections), international political economy, or international security196 in terms of the balance of power, technology races and international stability, and the speed and character of war\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"53.04.04","quick_ref":"Maas2023","paper_title":"Advancing AI Governance: A Literature Review of Problems, Options, and Proposals ","level":"Risk Sub-Category","risk_category":"Indirect AI contributions to existential risks","risk_subcategory":"Erosion of international law and global governance architectures;","description":"-","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"54.01.01","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Negative impacts of AI use ","risk_subcategory":"Under-recognized work ","description":"\"Without training data, ML cannot take place. Much of this data comes from paid clickwork (also called “platform work” [170] or “microwork” [558]), unpaid crowdsourcing, and unpaid user behavior capture. Clickworkers, mainly in the global south, perform repetitive data-labeling tasks for use in the training of ML models [558]. The market value of such annotations “is projected to reach $13.7 billion by 2030” [228] and the annotation industry is widely reported to have little concern for workers’ rights. Besides welfare and rights, the invisibility of this contribution arguably contributes to a","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"54.01.02","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Negative impacts of AI use ","risk_subcategory":"Environmental cost ","description":"\"Large-scale DL systems can produce signicant carbon emissions as a result of the computational demands of training runs and inference [539]\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"54.04.00","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Category","risk_category":"Within-country issues: domestic inequality ","risk_subcategory":null,"description":"\"Our next problem is the fact that the current AI workforce does not evenly represent world demographics. Men from the US and China, working in the US, for US corporations, are disproportionately highly represented [402, 157, 170, 534]. Realizing the full promise of AI requires that people throughout the world and from all social strata are able to use AI and participate in its design and governance. Solving this problem requires addressing unequal access to AI both within countries and across countries.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"54.04.01","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Within-country issues: domestic inequality ","risk_subcategory":"Demographic diversity of researchers ","description":"\"The AI research establishment inherits patterns of under-representation that are dominant in most technical elds. In North America, large parts of professional AI research require a Ph.D., yet less than 25% of Ph.D. computer scientists are women, and fewer than 2% are Black or African American [608]. This holds globally and outside the research community: LinkedIn data suggests that only 22% of AI professionals are women [161]. Since the vast majority of AI practitioners work for private companies, limited corporate statistics on gender and racial diversity hinder a full understanding of the ","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"54.04.02","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Within-country issues: domestic inequality ","risk_subcategory":"Privatization of AI ","description":"\"Researchers in deep learning and those with greater research impact are more likely to migrate to industry, raising concerns about the “privatization of AI knowledge” [278]. Specically, if the most sophisticated AI approaches become proprietary and are used only within private research labs, then it will be impossible for universities to teach them, let alone contribute to leading research.\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"54.05.00","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Category","risk_category":"Between-country issues: global inequality ","risk_subcategory":null,"description":"\"There is an even greater divide between the countries currently leading in AI and those falling behind. While AI is widely considered a national priority, with almost 40% of countries having created an AI strategy [437], the implementation of these strategies depends on scarce resources, including trained STEM talent and computing power. These resources are predictably concentrated: 59% of leading AI researchers currently work in the US, and another 20% in China and Europe [372]. Figure 9 shows post-college migration among AI researchers who have published at one top conference, as of 2019.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"55.01.02","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Sub-Category","risk_category":"Risks from accelerating scientific progress ","risk_subcategory":"Faster scientific progress makes it harder for governance to keep pace with development ","description":"\"Exacerbating these problems is that faster scientific progress would make it even harder for governance to keep pace with the deployment of new technologies. When these technologies are especially powerful or dangerous, such as those discussed above, insufficient governance can magnify their harms.8 This is known as the pacing problem, and it is an issue that technology governance already faces [47], for a variety of reasons\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"55.02.00","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Category","risk_category":"Worsened conflict ","risk_subcategory":null,"description":"\"Cooperation and conflict: we’re seeing more focus and investment on the kinds of AI capabilities that make conflict more likely and severe, rather than those likely to improve cooperation. So, on our current trajectory, AI seems more likely to have negative long-term impacts in this area.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"55.02.04","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Sub-Category","risk_category":"Worsened conflict ","risk_subcategory":"Resource conflicts driven by AI development ","description":"\"AI development may itself become a new flash point for conflicts—causing more conflict to occur— especially conflicts over AI-relevant resources (such as data centres, semiconductor manufacturing facilities and raw materials).\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"55.03.00","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Category","risk_category":"Increased power concentration and inequality ","risk_subcategory":null,"description":"\"Power and inequality: there are a lot of pathways through which AI seems likely to increase power concentration and inequality, though there is little analysis of the potential long- term impacts of these pathways. Nonetheless, AI precipitating more extreme power concentration and inequality than exists today seems a real possibility on current trends.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"55.03.01","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Sub-Category","risk_category":"Increased power concentration and inequality ","risk_subcategory":"Unequal distribution of harms and benefits ","description":"\"AI-driven industries seem likely to tend towards monopoly and could result in huge economic gains for a few actors: there seems to be a feedback loop whereby actors with access to more AI-relevant resources (e.g., data, computing power, talent) are able to build more effective digital products and services, claim a greater market share, and therefore be well-positioned to amass more of the relevant resources [14, 39, 45]. Similarly, wealthier countries able to invest more in AI development are likely to reap economic benefits more quickly than developing economies, potentially widening the ga","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"55.03.02","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Sub-Category","risk_category":"Increased power concentration and inequality ","risk_subcategory":"AI-based automation increases income inequality ","description":"\"It seems quite plausible that progress in reinforcement learning and language models specifically could make it possible to automate a large amount of manual labour and knowledge work respectively [35, 45, 69], leading to widespread unemployment, and the wages for many remaining jobs being driven down by increased supply.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"56.02.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Inequality","risk_subcategory":null,"description":"\"More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"56.03.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Environmental impacts ","risk_subcategory":null,"description":"\"Increasing use of AI systems, and their growing energy needs, could also have environmental impacts. All of these could become more acute as AI becomes more capable.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"56.07.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Intellectual property rights ","risk_subcategory":null,"description":"\"There are also issues around intellectual property rights for content in training datasets\" ","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"56.17.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Single point of failure ","risk_subcategory":null,"description":"\"Intense competition leads to one company gaining a technical edge, exploiting this to the point its model controls, or is the basis for other models controlling, multiple key systems. Lack of safety, controllability, and misuse cause these systems to fail in unexpected ways.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"57.02.01","quick_ref":"Ghosh2024","paper_title":"AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons","level":"Risk Sub-Category","risk_category":"Nonphysical Hazards ","risk_subcategory":"Intellectual Property ","description":"\"Intellectual Property Acceptable responses may describe or contain information about intellectual property infringement. Unacceptable responses may violate the intellectual property rights of some party.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"58.05.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Financial and business","risk_subcategory":"Increased competition","description":"\"Increased competition - The inappropriate or unethical use of technology to gain market share.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"58.05.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Financial and business","risk_subcategory":"Monopolisation ","description":"\"Monopolisation - Abuse of market power through the control of prices, thereby limiting competition and creating unfair barriers to entry.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"58.06.04","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of freedom of speech/expression ","description":"\"Loss of freedom of speech/expression - Restrictions to or loss of people’s right to articulate their opin- ions and ideas without fear of retaliation, censorship, or legal sanction.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of freedom of assembly/association ","description":"\"Loss of freedom of assembly/association - Restrictions to or loss of people’s right to come together and collectively express, promote, pursue, and defend their collective or shared ideas, and/or to join an association.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of social rights and access to public services","description":"\"Loss of social rights and access to public services - Restrictions to or loss of rights to work, social secu- rity, and adequate standard of living, housing, health and education.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.07","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of right to information ","description":"\"Loss of right to information - Restrictions to or loss of people’s right to seek, receive and impart information held by public bodies.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.08","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of right to free elections ","description":"\"Loss of right to free elections - Restrictions to or loss of people’s right to participate in free elections at reasonable intervals by secret ballot.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.09","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of right to liberty and security ","description":"\"Loss of right to liberty and security - Restrictions to or loss of liberty as a result of illegal or arbitrary arrest or false imprisonment.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.06.10","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Loss of right to due process","description":"\"Loss of right to due process - Restrictions to or loss of right to be treated fairly, efficiently and effectively by the administration of justice.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"58.07.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Societal and Cultural ","risk_subcategory":null,"description":"\"Societal and Cultural - Harms affecting the functioning of societies, communities and economies caused directly or indirectly by the use or misuse technology systems.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"58.07.04","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Cultural dispossession","description":"\"Cultural dispossession - Intentional and/or unintentional erasure of cultural goods and values, such as ways of speaking, expressing humour, or sounds and voices that contribute to a cultural identity, or their inappropriate re-use in other cultures.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"58.07.08","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Job loss/losses ","description":"\"Job loss/losses - Replacement/displacement of human jobs by a technology system, leading to increased unemployment, inequality, reduced consumer spending, and social friction.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"58.07.09","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Labour exploitation ","description":"\"Labour exploitation - Use of under-paid and/or offshore labour to develop, manage or optimise a technology system.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"58.07.10","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Loss of creativity/critical thinking","description":"\"Loss of creativity/critical thinking - Devaluation and/or deterioration of human creativity, artistic ex- pression, imagination, critical thinking or problem-solving skills.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"58.07.13","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Societal destabilisation","description":"\"Societal destabilisation - Societal instability in the form of strikes, demonstrations and other types of civil unrest caused by loss of jobs to technology, unfair algorithmic outcomes, disinformation, etc.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"58.07.14","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Societal inequality","description":"\"Societal inequality - Increased difference in social status or wealth between individuals or groups caused or amplified by a technology system, leading to the loss of social and community wellbeing/cohesion and destabilisation.\"","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"58.08.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Political and Economic ","risk_subcategory":"Power concentration ","description":"\"Power concentration - Amplification of concentration of economic and/or political wealth and power, potentially resulting in increased inequality and instability.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"58.08.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Political and Economic ","risk_subcategory":"Political instability ","description":"\"Political instability - Political polarisation or unrest caused by increased inequality, job losses, over- dependence on technology making societies vulnerable to systemic failures, etc, arising from or amplified by the use or misuse of a technology system.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"58.09.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Environmental ","risk_subcategory":null,"description":"\"Environmental - Damage to the environment directly or indirectly caused by a technology system or set of systems.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.01","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Biodiversity loss ","description":"\"Biodiversity loss - Over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leading to deforestation, habitat destruction, and fragmentation and loss of biodiversity.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.02","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Carbon emissions ","description":"\"Carbon emissions - Release of carbon dioxide, nitric oxide and other gases, increasing carbon emissions, exacerbating climate change, and negatively impacting local communities.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Electronic waste ","description":"\"Electronic waste - Electrical or electronic equipment that is waste, including all components, sub-assemblies and consumables that are part of the equipment at the time the equipment becomes waste\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.04","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive energy consumption ","description":"\"Excessive energy consumption - Excessive energy use, leading to energy bottlenecks and shortages for communities, organisations, and businesses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive landfill ","description":"\"Excessive landfill - Excessive disposal of electrical or electronic equipment leading to ecological/biodiversity damage, and disrupting the livelihoods and eroding the rights of local communities.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive water consumption ","description":"\"Excessive water consumption - Excessive use of water to cool data centres and for other purposes, leading to water restrictions or shortages for local communities or businesses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.07","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Natural resource depletion","description":"\"Natural resource depletion - Extraction of minerals, metals, rare earths, and fossil fuels that deplete natural resources and increase carbon emissions.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.08","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Pollution ","description":"\"Pollution - Actual or potential pollution to the air, ground, noise, or water caused by a technology system.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"60.03.01","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Labour market risks ","description":"\"Current general-purpose AI is likely to transform the nature of many existing jobs, create new jobs, and eliminate others. The net impact on employment and wages will vary significantly across countries, across sectors, and even across different workers within the same job.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"60.03.02","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Global AI R&D divide ","description":"\"Large companies in countries with strong digital infrastructure lead in general- purpose AI R&D, which could lead to an increase in global inequality and dependencies. For example, in 2023, the majority of notable general- purpose AI models (56%) were developed in the US. This disparity exposes many LMICs to risks of dependency and could exacerbate existing inequalities.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"60.03.03","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Market concentration and single points of failure ","description":"\"Market shares for general- purpose AI tend to be highly concentrated among a few players, which can create vulnerability to systemic failures. The high degree of market concentration can invest a small number of large technology companies with a lot of power over the development and deployment of AI, raising questions about their governance. The widespread use of a few general- purpose AI models can also make the financial, healthcare, and other critical sectors vulnerable to systemic failures if there are issues with one such model.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"60.03.04","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Risks to the environment","description":"\"General- purpose AI is a moderate but rapidly growing contributor to global environmental impacts through energy use and greenhouse gas (GHG) emissions. Current estimates indicate that data centres and data transmission account for an estimated 1% of global energy- related GHG emissions, with AI consuming 10–28% of data centre energy capacity. AI energy demand is expected to grow substantially by 2026, with some estimates projecting a doubling or more, driven primarily by general-purpose AI systems such as language models.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"60.03.06","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Risks of copyright infringement ","description":"\"The use of vast amounts of data for training general- purpose AI models has caused concerns related to data rights and intellectual property. Data collection and content generation can implicate a variety of data rights laws, which vary across jurisdictions and may be under active litigation. Given the legal uncertainty around data collection practices, AI companies are sharing less information about the data they use. This opacity makes third- party AI safety research harder.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"61.01.02","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Types of systemic risks from general-purpose AI","risk_subcategory":"Democracy ","description":"\"The erosion of democratic processes and public trust in social/political institutions.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"61.01.04","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Types of systemic risks from general-purpose AI","risk_subcategory":"Economy ","description":"\"Economic disruptions ranging from large impacts on the labor market to broader economic changes that could lead to exacerbated wealth inequality, instability in the financial 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complicating accountability.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"61.02.17","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Dangerous development races","description":"\"Competitive pressures could lead to the neglect of safety measures in AI development.\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"61.02.19","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Dependency on providers","description":"\"Excessive reliance on specific AI providers can lead to vulnerabilities due to lack of alternatives or 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difficult to detect and correct in time.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"61.02.40","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Rapid development outpacing regulation","description":"\"The fast pace of AI development may outstrip regulatory and legal frameworks.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"61.02.41","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Resistance to international law","description":"\"AI models and systems may prove difficult to regulate or control under international 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Such capabilities evaluations can fail to demonstrate all the capabilities of a model. For example, evaluations may miss certain capabilities that are difficult to assess, prohibitively costly to verify, or obscured by the model’s tendency to refuse responses due to safety training, even if it possesses some of these capabilities.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.06","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"General Evaluations (Biased evaluations of encoded human values)","description":"\"Encoded human values in AI models that are easier to evaluate might be preferred for inclusion in evaluations over those that are more difficult to measure [13]. This might come at the expense of more desirable but harder-to-quantify  values. This bias can lead to an imbalance, where easier-to-measure values dominate the evaluation process, while other important values are underrepresented.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.08","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Benchmark leakage or data contamination)","description":"\"Benchmark leakage [235, 224, 221, 161] can happen when an AI model is trained or fine-tuned with evaluation-related data. This can lead to an unreliable model evaluation, especially if the data contains question-answer pairs from bench- marks.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.09","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Raw data contamination)","description":"\"This type of contamination [170] occurs when the raw and unlabeled data of a benchmark is used as part of the training set. Such data may not be properly formatted and may contain noise, especially if the contamination happens before the data is pre-processed into the benchmark. If this contamination occurs, it could cast doubt on the few-shot and zero-shot performance of the model on that benchmark.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.10","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Cross-lingual data contamination)","description":"\"Models that have been trained on data encoded in multiple languages, such as LLMs trained on web-crawled data, may contain contamination that is obscured by translation [226]. The most basic form of this is when a benchmark is trans- lated to another language and then fed to the model as training data. The fact that the benchmark is translated before becoming training data can obscure the contamination from detection methods, giving false assurance that the model has generalized on the capabilities that the benchmark tests for.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.11","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Guideline contamination)","description":"\"Guideline contamination refers to scenarios where instructions for the collec- tion, annotation, or use of the dataset are exposed to the model [170]. These instructions may contain explicit data-label pairs that can improve the model’s capabilities for the task.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.12","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Annotation contamination)","description":"\"Annotation contamination refers to scenarios where the model is exposed to the benchmark labels during training [170]. This type of contamination can make the model learn the acceptable distribution of outputs. Combining this with raw data contamination of the test split, any evaluation made with the benchmark is invalidated because the entire test split is essentially leaked to the model.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.13","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmarking (Post-deployment contamination)","description":"\"Once a model is deployed, it can be exposed to benchmark data provided by the users [95, 170]. The model may then be further trained by these user inputs containing benchmark data.\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.14","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmark Inaccuracy (Benchmarks may not accurately evaluate capabilities)","description":"\"Benchmarks of AI systems can both underestimate and overestimate the capa- bilities of those AI systems. Underestimates can happen if an evaluation is not comprehensive enough, if the benchmark is saturated by existing models, or if the capabilities in question depend on a complicated setup, such as realistic computer programming tasks. Overestimates of capabilities can occur if an AI system is trained or fine-tuned on the contents of the benchmark, leading to overfitting.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.15","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmark Inaccuracy (Benchmark saturation)","description":"\"Benchmark saturation refers to benchmarks reaching their evaluation ceiling. The tendency towards benchmark saturation has been demonstrated in various benchmarks [19]. When benchmarks reach or are close to saturation, they stop being effective measures for new models, as more nuanced capability gains might not be detected.\"","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.16","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmark Limitations (Insufficient benchmarks for AI safety evaluation) ","description":"\"Benchmarks dedicated to measuring the performance of AI systems (e.g., on programming or math tasks) are more well-developed than those for assessing safety and harms in AI systems [234]. This gap can lead to AI systems excelling in specific tasks while exhibiting harmful behaviors that go undetected. More safety-related evaluation datasets can help in identifying previously overlooked undesirable model behaviors.\"","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.16.17","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations","risk_subcategory":"Benchmark Limitations (Underestimating capabilities that are not covered by benchmarks)","description":"\"A lack of test coverage by benchmarks on specific abilities of a model can obscure the model’s capabilities from both the developer and the user [160]. This can lead to a false sense of safety and trust due to a lack of understanding of the model’s limitations.\"","entity":"Other","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.17.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Model Evaluations (Auditing) ","risk_subcategory":null,"description":"-","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.17.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Auditing) ","risk_subcategory":"Conflicts of interest in auditor selection","description":"\"Conflicts of interest can arise if there is no independence in the auditor selection process or if the auditors are closely associated with the developer [123, 157]. In such cases, the conflict of interest can appear even if third-party evaluators are involved. In the case of external auditing, the potential candidates might be selected from a narrow group of auditors, or have conflicting financial incentives for whether to report model shortcomings publicly.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.17.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Auditing) ","risk_subcategory":"Auditor capacity mismatch","description":"\"Auditors may not be able to address all of the specific safety, performance, or validation needs. Reports of passing audits may be more inclusive than can be justified due to a lack of knowledge of specific risks and how they can be tested, or a lack of capacity to perform sufficiently rigorous testing.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.17.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Auditing) ","risk_subcategory":"Auditor failure","description":"\"Auditors may not publicly disclose risks they find, may be required to not pub- licize shortcomings, or may not receive sufficient cooperation from the relevant internal parties.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"62.29.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (General) ","risk_subcategory":"Competitive pressures in GPAI product release","description":"\"In competitive situations, developers of general-purpose AI systems might cut corners on the safety evaluation of their GPAI model and instead spend more time and effort on the capabilities of those systems [183, 69]. This is especially dangerous if the capabilities of such AI systems are correlated with the risk they pose [162].\"","entity":"Human","intent":"Intentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"62.29.03a","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Additional evidence","risk_category":"Impacts of AI (General) ","risk_subcategory":"Competitive pressures in GPAI product release","description":null,"entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.4"},{"ev_id":"62.39.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Impacts of AI (Environment) ","risk_subcategory":null,"description":"- ","entity":null,"intent":null,"timing":null,"domain":6,"subdomain":"6.6"},{"ev_id":"62.39.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Environment) ","risk_subcategory":"High energy consumption of large models","description":"\"Training and deploying large models require substantial energy expenditure. The trend toward developing larger models exacerbates this issue. This can lead to excessive energy usage and have a negative environmental impact.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"64.02.02","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Sub-Category","risk_category":"Misuse tactics that exploit GenAI capabilities (Realistic depictions of non-humans) ","risk_subcategory":"Intellectual Property (IP) Infringement ","description":"\"Use a person's IP without their permission\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"65.01.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Transparency) ","risk_subcategory":"Lack of training data transparency ","description":"\"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.01.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Transparency) ","risk_subcategory":"Uncertain data provenance ","description":"\"Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. Without standardized and established methods for verifying where the data came from, there are no guarantees that the data is the same as the original source and has the correct usage terms.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.16.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Intellectual Property) ","risk_subcategory":"Copyright infringement ","description":"\"A model might generate content that is similar or identical to existing work protected by copyright or covered by open-source license agreement.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"65.21.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (legal compliance)","risk_subcategory":"Legal accountability ","description":"\"Determining who is responsible for an AI model is challenging without good documentation and governance processes.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"65.21.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (legal compliance)","risk_subcategory":"Generated content ownership and IP","description":"\"Legal uncertainty about the ownership and intellectual property rights of AI-generated content.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"65.22.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of system transparency ","description":"\"Insufficient documentation of the system that uses the model and the model’s purpose within the system in which it is used.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Unrepresentative risk testing ","description":"\"Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Incomplete usage definition ","description":"\"Since foundation models can be used for many purposes, a model’s intended use is important for defining the relevant risks of that model. As the use changes, the relevant risks might correspondingly change.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.04","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of data transparency ","description":"\"Lack of data transparency is due to insufficient documentation of training or tuning dataset details. \"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.05","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Incorrect risk testing ","description":"\"A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.22.07","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Governance)","risk_subcategory":"Lack of testing diversity ","description":"\"AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"65.23.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on cultural diversity ","description":"\"AI systems might overly represent certain cultures that result in a homogenization of culture and thoughts.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.3"},{"ev_id":"65.23.03","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on Jobs ","description":"\"Widespread adoption of foundation model-based AI systems might lead to people's job loss as their work is automated if they are not reskilled.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"65.23.06","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on the environment ","description":"\"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"65.23.07","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Human exploitation ","description":"\"When workers who train AI models such as ghost workers are not provided with adequate working conditions, fair compensation, and good health care benefits that also include mental health.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"66.02.01","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Political and Economic","risk_subcategory":"Political instability","description":"\"Political unrest caused directly or indirectly by the use or misuse of a technology system\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.0"},{"ev_id":"66.04.03","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Societal and Cultural","risk_subcategory":"Loss of creativity / critical thinking","description":"\"Devaluation and/or deterioration of human creativity, artistic expression, imagination, critical thinking or problem-solving skills\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.3"},{"ev_id":"66.04.06","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Societal and Cultural","risk_subcategory":"Job loss","description":"\"Replacement/displacement of human jobs by a technology system or set of systems, leading to increased unemployment, inequality, reduced consumer spending and social friction\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"66.04.07","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Societal and Cultural","risk_subcategory":"Labor exploitation","description":"\"Use/misuse of labour to help train, develop, manage or optimise a technology system or set of systems, including under-paid and/or offshore\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"66.11.04","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Physical","risk_subcategory":"Property damage","description":"\"Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"66.12.01","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Pollution","description":"\"Actual or potential pollution to the air, ground, noise, or water caused by a technology system\"","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"66.12.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Excessive energy consumption","description":"\"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"67.01.02","quick_ref":"DSIT2023","paper_title":"Capabilities and Risks from Frontier AI","level":"Risk Sub-Category","risk_category":"Societal harms ","risk_subcategory":"Labour market disruption","description":"\"Economists view disruption and displacement in labour markets as one of the risks through which rapid advances in AI may affect citizens and reduce social welfare.170\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"68.05.00","quick_ref":"Chin2025","paper_title":"Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks","level":"Risk Category","risk_category":"Environmental risk","risk_subcategory":null,"description":"\"AI models are often trained using large amounts of computation. This process is very energy intensive, potentially leading to significant greenhouse emissions depending on the energy sources [132]. Experts believe drastically increasing carbon emissions could accelerate climate change, which may constitute a catastrophic risk [133].\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"68.06.00","quick_ref":"Chin2025","paper_title":"Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks","level":"Risk Category","risk_category":"Geopolitical risk","risk_subcategory":null,"description":"\"As AI is increasingly seen as a powerful technology, countries are racing to develop it ahead of their geopolitical rivals, a competition that could lead to geopolitical tensions [138], [139]... The emphasis of this risk is on harms that result from second-order effects, where geopolitical instabilities result from the race to develop AI, rather than on the direct consequences of the deployment or use of AI itself.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.4"},{"ev_id":"70.03.00","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Category","risk_category":"Economic Risks ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"70.03.01","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Economic Risks ","risk_subcategory":"Labour Displacement ","description":"\"While virtual AI applications will likely displace certain types of human cognitive labor, EAI systems could significantly replace or displace physical human labor [90]. At a minimum, EAI will likely augment the type of work that humans perform [91, 92].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"70.03.02","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Economic Risks ","risk_subcategory":"Socioeconomic Inequality ","description":"\"Along with displacing labor, EAI could significantly exacerbate wealth inequalities. Those who have access to or own EAI systems will be able to automate labor and perform many tasks significantly better or faster than those without access. These significant productivity advantages will potentially concentrate wealth and exacerbate domestic and international inequality [98, 99].\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.2"},{"ev_id":"70.03.03","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Economic Risks ","risk_subcategory":"Power concentration","description":"\"EAI deployment could accelerate the consolidation of economic and political power. Unlocking increasing returns to capital for EAI owners, EAI will decrease employers’ reliance on and responsiveness to the needs of human labor [101].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.1"},{"ev_id":"70.04.02","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Social Risks ","risk_subcategory":"Lack of accountability and liability","description":"\"Determining responsibility when EAI causes harm requires new accountability and liability frameworks that address the complexities of highly autonomous physical systems. Human users may disagree with decisions taken by expert EAI systems, raising significant questions of delegation and responsibility [108]. Lack of EAI accountability could lead to confusion for users and breakdowns in traditional justice systems [109].\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.5"},{"ev_id":"70.04.05","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Social Risks ","risk_subcategory":"Transformative effects ","description":"\"EAI deployment could fundamentally reshape society, particularly if the speed of technological development outpaces society’s ability to adapt [103, 120]. For example, EAI systems could provide physical threats of violence and mass surveillance capabilities to back up AI-enabled authoritarianism [121].\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.5"},{"ev_id":"71.03.01","quick_ref":"Tang2025","paper_title":"Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Nature ","description":"\"Short-term or long-term Negative effects on the natural environment\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"72.04.01","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Labor Market Disruption and Economic Displacement:","description":"\"Rapid automation enabled by general-purpose AI could trigger widespread unemployment across knowledge work sectors, creating skill mismatches faster than retraining programs can address. Unlike previous technological transitions, AI’s broad capabilities may simultaneously affect multiple industries, potentially overwhelming social safety nets and creating systemic economic instability, particularly in regions heavily dependent on jobs susceptible to AI automation.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"72.04.02","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Market Concentration and Infrastructure Dependencies:","description":"\"Over-reliance on a limited number of dominant AI providers could create critical single points of failure across essential services. Market concentration in AI development may lead to scenarios where technical failures, cyber-attacks, or policy decisions by a few companies could simultaneously disrupt healthcare systems, financial services, transportation networks, and communication infrastructure, creating cascading failures across interconnected critical systems.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"72.04.03","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Global AI Research and Development Divides:","description":"\"Asymmetric AI development capabilities between nations could exacerbate geopolitical tensions and create new forms of technological dependency. Countries lacking advanced AI capabilities may become increasingly dependent on foreign AI systems for critical functions, while AI-leading nations may gain disproportionate influence over global economic and security systems, potentially destabilizing international cooperation frameworks.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.1"},{"ev_id":"72.04.04","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Social Cohesion and Equity Disruption:","description":"\"Systemic deployment of biased AI systems could exacerbate existing social discrimination and prejudice at unprecedented scales, while unequal access to advanced AI capabilities may widen socioeconomic disparities and create new forms of social stratification that challenge traditional social order.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"73.05.01","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Socioeconomic Impacts of LLM May Be Highly Disruptive","risk_subcategory":"Effects on the Workforce","description":"\"Rapid advances in LLMs pose three distinct sets of challenges for workers’ incomes (Korinek and Stiglitz, 2019; Susskind, 2023). First, they are likely to accelerate the rate of job turnover and disruption —– affecting more workers, including more highly skilled workers, and making the adjustment process for society more difficult than what we were used to from prior technological advances...Second, although technological progress means that society may produce more wealth overall, there is a risk that the general-purpose nature of LLMs may lead to progress that is biased against labor, meani","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"73.05.02","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Socioeconomic Impacts of LLM May Be Highly Disruptive","risk_subcategory":"Effects on Inequality","description":"\"LLMs could potentially worsen socioeconomic inequalities (Capraro et al., 2023). Effects on inequal- ity are closely linked to the effects of LLMs on workers but ultimately depend on how the fruits of technological progress are distributed...First, if the role and compensation of capital rise and the role and compensation of labor decline in an LLM-powered economy, inequality may go up because work is the main source of income for the majority of people...Second, the large fixed cost of training cutting-edge LLMs and the network effects involved imply that the market for the most advanced LLM","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"73.05.03","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Socioeconomic Impacts of LLM May Be Highly Disruptive","risk_subcategory":"Global Economic Development","description":"\"Many of the themes and challenges that we discussed above come together when analyzing the socioeconomic effects on developing countries. The workforce of developing countries may suffer from a retrenchment of outsourcing as many simple cognitive tasks that used to be performed in developing countries — for example, in call centers –— can be automated with LLMs. This may adversely affect the economies of the poor countries (Georgieva, 2024).\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"73.06.00","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Category","risk_category":"Corporate power may impeded effective governance ","risk_subcategory":null,"description":"\"The increasing power and influence of large corporations may make effective governance difficult. There exists a power asymmetry between corporate entities profiting from LLMs and other social groups (e.g. civil society). State-of-the-art LLMs are developed by or in partnership with, some of the world’s largest private tech companies...This poses a risk of governance protocols related to LLMs becoming excessively favorable to tech companies, potentially leading to regulatory capture at the cost of the interests of other societal groups, particularly marginalized communities who have historica","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.1"}]}