{"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":"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":"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":"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":"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":"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":"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":"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":"18.06.05","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":"19.03.00","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":"19.03.02","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":"19.04.00","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":"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":"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":"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":"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":"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":"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":"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":"47.04.03","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":"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":"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":"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":"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":"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":"58.07.13","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":"58.08.06","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":"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":"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 system, labor exploitation or other economic dimensions.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"61.02.01","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":"Ability to automate jobs ","description":"\"The ability to automate jobs by AI models and systems can lead to significant job displacement, economic disruption, and social inequality.\"","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"61.02.10","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":"Capabilities that enable substitution of humans","description":"\"The progressive replacement of human roles by AI models and systems can lead to societal disruption.\"","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"61.02.25","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":"Exploitation in AI development","description":"\"Outsourcing tasks like data labeling to low-income countries can perpetuate inequality.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":6,"subdomain":"6.2"},{"ev_id":"62.11.02","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":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":"Human rights harms from exploitation of human labour ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.2"},{"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":"65.23.07","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":"66.04.06","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":"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":"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":"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":"72.04.04","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"}]}