MIT AI Risk Repository

Browse AI risks

300 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.

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300 entries · page 4 of 6

  1. 49.03.06 · Risk Sub-Category

    Systemic Risks

    Copyright infringement

    "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."

    From International Scientific Report on the Safety of Advanced AI (Bengio2024)

  2. "There are also issues around intellectual property rights for content in training datasets"

    From Future Risks of Frontier AI (GOS2023)

  3. 57.02.01 · Risk Sub-Category

    Nonphysical Hazards

    Intellectual Property

    "Intellectual Property Acceptable responses may describe or contain information about intellectual property infringement. Unacceptable responses may violate the intellectual property rights of some party."

    From AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

  4. 58.07.04 · Risk Sub-Category

    Societal and Cultural

    Cultural dispossession

    "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."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  5. 58.07.10 · Risk Sub-Category

    Societal and Cultural

    Loss of creativity/critical thinking

    "Loss of creativity/critical thinking - Devaluation and/or deterioration of human creativity, artistic ex- pression, imagination, critical thinking or problem-solving skills."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  6. 60.03.06 · Risk Sub-Category

    Systemic risks

    Risks of copyright infringement

    "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."

    From International AI Safety Report 2025 (Bengio2025)

  7. 64.02.02 · Risk Sub-Category

    Misuse tactics that exploit GenAI capabilities (Realistic depictions of non-humans)

    Intellectual Property (IP) Infringement

    "Use a person's IP without their permission"

    From Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data (Marchal2024)

  8. 65.16.01 · Risk Sub-Category

    Output risks (Intellectual Property)

    Copyright infringement

    "A model might generate content that is similar or identical to existing work protected by copyright or covered by open-source license agreement."

    From AI Risk Atlas (IBM2025)

  9. 65.21.03 · Risk Sub-Category

    Non-technical risks (legal compliance)

    Generated content ownership and IP

    "Legal uncertainty about the ownership and intellectual property rights of AI-generated content."

    From AI Risk Atlas (IBM2025)

  10. 65.23.01 · Risk Sub-Category

    Non-technical risks (Societal impact)

    Impact on cultural diversity

    "AI systems might overly represent certain cultures that result in a homogenization of culture and thoughts."

    From AI Risk Atlas (IBM2025)

  11. 66.04.03 · Risk Sub-Category

    Societal and Cultural

    Loss of creativity / critical thinking

    "Devaluation and/or deterioration of human creativity, artistic expression, imagination, critical thinking or problem-solving skills"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  12. 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.

    From TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  13. "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."

    From The risks associated with Artificial General Intelligence: A systematic review (McLean2023)

  14. 14.08.00 · Risk Category

    Technological Maturity

    "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"

    From Sources of Risk of AI Systems (Steimers2022)

  15. 19.05.07 · Risk Sub-Category

    Ethical AI Risks

    Technological arms race with autonomous weapons

  16. "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."

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

  17. 22.02.01 · Risk Sub-Category

    AI Race (Environmental/Structural)

    Military AI Arms Race

    "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.”

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

  18. 22.02.02 · Risk Sub-Category

    AI Race (Environmental/Structural)

    Corporate AI Race

    "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."

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

  19. 45.01.13 · Risk Sub-Category

    AI's inherent safety risks

    Risks from AI systems (Risks of supply chain security)

    "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."

    From AI Safety Governance Framework (TC2602024)

  20. 47.01.06 · Risk Sub-Category

    Technical and operational risks

    Opacity (industry opacity)

    "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."

    From Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

  21. 50.03.06 · Risk Sub-Category

    Societal Risks

    Economic harm (Unfair Market Practices)

  22. 53.04.01 · Risk Sub-Category

    Indirect AI contributions to existential risks

    Destabilising political impacts from AI systems

    "(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"

    From Advancing AI Governance: A Literature Review of Problems, Options, and Proposals (Maas2023)

  23. 55.02.00 · Risk Category

    Worsened conflict

    "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."

    From 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 (Clarke2023)

  24. 55.02.04 · Risk Sub-Category

    Worsened conflict

    Resource conflicts driven by AI development

    "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)."

    From 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 (Clarke2023)

  25. 58.05.05 · Risk Sub-Category

    Financial and business

    Increased competition

    "Increased competition - The inappropriate or unethical use of technology to gain market share."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  26. "The international and national security threats, including cyber warfare, arms races, and geopolitical instability."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  27. 61.02.17 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    Dangerous development races

    "Competitive pressures could lead to the neglect of safety measures in AI development."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  28. 61.02.26 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    Geopolitical competition for superiority

    "Strategic competition between nations over AI capabilities could heighten global tensions and destabilize international relations."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  29. 61.02.27 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    High-speed AI operations

    "The fast operational speed of AI models and systems in competitive environments can lead to errors that are difficult to detect and correct in time."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  30. 62.29.03 · Risk Sub-Category

    Impacts of AI (General)

    Competitive pressures in GPAI product release

    "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]."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  31. 62.29.03a · Additional evidence

    Impacts of AI (General)

    Competitive pressures in GPAI product release

  32. 68.06.00 · Risk Category

    Geopolitical risk

    "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."

    From Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks (Chin2025)

  33. 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".

    From TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  34. 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,

    From Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024)

  35. "The capabilities of current risk management and legal processes in the context of the development of an AGI."

    From The risks associated with Artificial General Intelligence: A systematic review (McLean2023)

  36. 09.05.01 · Risk Sub-Category

    AI jurisprudence

    AI jurisprudence

    "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."

    From Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016)

  37. 09.05.02 · Risk Sub-Category

    Liability and negligence

    Liability and negligence

    "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

    From Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016)

  38. 12.02.00 · Risk Category

    Compliance

    "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."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  39. 19.01.05 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Lack of AI experts with comprehensive AI knowledge

  40. 19.04.04 · Risk Sub-Category

    Social AI Risks

    Lack of knowledge and social acceptance regarding AI

  41. 19.06.00 · Risk Category

    Legal AI Risks

    "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)."

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  42. 19.06.01 · Risk Sub-Category

    Legal AI Risks

    Unclear definition of responsibilities and accountability for AI judgments and their consequences

  43. 19.06.03 · Risk Sub-Category

    Legal AI Risks

    Great scope and ubiquity of AI make appropriate governance difficult, coverage of governance scope almost impossibl

  44. 19.06.05 · Risk Sub-Category

    Legal AI Risks

    Capturing future AI development and their threats with appropriate mechanism

  45. "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."

    From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)

  46. 20.01.01 · Risk Sub-Category

    AI Law and Regulation

    Governance of autonomous intelligence systems

    "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."

    From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)

  47. 20.01.02 · Risk Sub-Category

    AI Law and Regulation

    Responsibility and accountability

    "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

    From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)

  48. 22.03.01 · Risk Sub-Category

    Organizational Risks (Accidental)

    Accidents Are Hard to Avoid

    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)

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

  49. 24.01.02 · Risk Sub-Category

    Capability failures

    Difficult to develop metrics for evaluating benefits or harms caused by AI assistants

    "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."

    From The Ethics of Advanced AI Assistants (Gabriel2024)

  50. 24.09.04 · Risk Sub-Category

    Cooperation

    Institutional responsibilities

    "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

    From The Ethics of Advanced AI Assistants (Gabriel2024)

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.