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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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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11.05.00 · Risk Category
"Social system or societal harms reflect the adverse macro-level effects of new and reconfigurable algorithmic systems, such as systematizing bias and inequality [84] and accelerating the scale of harm [137]"
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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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The risk of financial and/or reputational damage to the organization building or using the ML system.
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16.06.00 · Risk Category
"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
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17.06.00 · Risk Category
"Harms that arise from environmental or downstream economic impacts of the language model"
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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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47.04.00 · Risk Category
"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."
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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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58.07.00 · Risk Category
"Societal and Cultural - Harms affecting the functioning of societies, communities and economies caused directly or indirectly by the use or misuse technology systems."
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"The erosion of democratic processes and public trust in social/political institutions."
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"Political unrest caused directly or indirectly by the use or misuse of a technology system"
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70.03.00 · Risk Category
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"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."
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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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"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."
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"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."
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16.06.04 · Risk Sub-Category
Risk area 6: Environmental and Socioeconomic harms
Disparate access to benefits due to hardware, software, skill constraints
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
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17.06.04 · Risk Sub-Category
Automation, Access and Environmental Harms
Disparate access to benefits due to hardware, software, skills constraints
"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."
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18.06.01 · Risk Sub-Category
Socioeconomic and environmental harms
Unfair distribution of benefits from model access
"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)"
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19.01.07 · Risk Sub-Category
Technological, Data and Analytical AI Risks
High investment costs of AI hinder integration
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19.03.04 · Risk Sub-Category
Financial feasibility and high investment costs for AI technology to remain competitive
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19.03.05 · Risk Sub-Category
Lack of AI strategy and acceptance/resistance among employees and customers
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19.06.04 · Risk Sub-Category
Hard legislation on AI hinders innovation processes and further AI development
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"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"
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"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
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24.10.00 · Risk Category
"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.
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24.10.01 · Risk Sub-Category
Entrenchment and exacerbation of existing inequalities
"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.
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"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
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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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"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
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31.09.00 · Risk Category
"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."
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"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
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35.05.00 · Risk Category
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
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37.01.00 · Risk Category
"ethical concerns regarding how AI is designed and who designs it"
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"This group of concerns represents 2% of the sample and highlights two central issues: Western centrality and cultural difference, and unequal participation."
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"The political influence and competitive advantage obtained by having technology."
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47.04.01 · Risk Sub-Category
Environmental, economical, and societal challenges
Concentration of market power (Trend toward market concentration)
"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."
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47.04.02 · Risk Sub-Category
Environmental, economical, and societal challenges
Concentration of market power (Negative effects of increased market concentration)
"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
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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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"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."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.