MIT AI Risk Repository
Browse AI risks
336 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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07.04.00 · Risk Category
"Structural risks are concerned with how AI technologies "shape and are shaped by the environments in which they are developed and deployed""
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12.03.00 · Risk Category
"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."
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15.02.00 · Risk Category
"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."
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18.06.00 · Risk Category
"AI systems amplifying existing inequalities or creating negative impacts on employment, innovation, and the environment"
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"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."
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58.06.05 · Risk Sub-Category
Human rights and civil liberties
Loss of freedom of assembly/association
"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."
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58.06.06 · Risk Sub-Category
Human rights and civil liberties
Loss of social rights and access to public services
"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."
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"Loss of right to information - Restrictions to or loss of people’s right to seek, receive and impart information held by public bodies."
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"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."
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"Loss of right to liberty and security - Restrictions to or loss of liberty as a result of illegal or arbitrary arrest or false imprisonment."
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"Loss of right to due process - Restrictions to or loss of right to be treated fairly, efficiently and effectively by the administration of justice."
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"The erosion of democratic processes and public trust in social/political institutions."
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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
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"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
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"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."
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52.03.00 · Risk Category
"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."
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54.04.00 · Risk Category
"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."
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54.04.01 · Risk Sub-Category
Within-country issues: domestic inequality
Demographic diversity of researchers
"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
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54.05.00 · Risk Category
"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."
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"Power concentration - Amplification of concentration of economic and/or political wealth and power, potentially resulting in increased inequality and instability."
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"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."
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"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."
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61.02.50 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Winner-take-all dynamics
"The competitive nature of AI development could lead to significant eco- nomic and security advantages for a few entities."
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"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."
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73.06.00 · Risk Category
"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
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19.03.00 · Risk Category
"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.
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"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)"
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"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."
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"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
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"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."
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"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."
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"Replacement/displacement of human jobs by a technology system or set of systems, leading to increased unemployment, inequality, reduced consumer spending and social friction"
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"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"
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73.05.01 · Risk Sub-Category
Socioeconomic Impacts of LLM May Be Highly Disruptive
Effects on the Workforce
"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
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73.05.03 · Risk Sub-Category
Socioeconomic Impacts of LLM May Be Highly Disruptive
Global Economic Development
"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)."
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03.03.00 · Risk Category
"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
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"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."
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"Loss of creativity/critical thinking - Devaluation and/or deterioration of human creativity, artistic ex- pression, imagination, critical thinking or problem-solving skills."
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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."
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"Devaluation and/or deterioration of human creativity, artistic expression, imagination, critical thinking or problem-solving skills"
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14.08.00 · Risk Category
"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"
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"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)."
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"The international and national security threats, including cyber warfare, arms races, and geopolitical instability."
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68.06.00 · Risk Category
"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."
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19.06.00 · Risk Category
"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)."
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19.06.03 · Risk Sub-Category
Great scope and ubiquity of AI make appropriate governance difficult, coverage of governance scope almost impossibl
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"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."
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"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
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.