MIT AI Risk Repository
Browse AI risks
554 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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"One concern here is humans not remaining in the loop for some military decisions, creating the possibility of unintentional escalation because of: • Automated tactical decision-making, by ‘in-theatre’ AI systems (e.g. border patrol systems start accidentally firing on one another), leading to either: tactical-level war crimes,11 or strategic-level decisions to initiate conflict or escalate to a higher level of intensity—for example, countervalue (e.g. city-) targeting, or going nuclear [62]. • Automated strategic decision-making, by ‘out-of-theatre’ AI systems—for example, conflict prediction
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"For example, AI could undermine nuclear strategic stability by making it easier to discover and destroy previously secure nuclear launch facilities [30, 46, 49]. AI may also offer more extreme first-strike advantages or novel destructive capabilities that could disrupt deterrence, such as cyber capabilities being used to knock out opponents’ nuclear command and control [15, 29]. The use of AI capabilities may make it less clear where attacks originate from, making it easier for aggressors to obfuscate an attack, and therefore reducing the costs of initiating one. By making it more difficult t
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56.18.00 · Risk Category
"As AI capability increases, humans grant AI more control over critical systems and eventually become irreversibly dependent on systems they don’t fully understand. Failure and unintended outcomes cannot be controlled."
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62.31.02#1 · Risk Sub-Category
Impacts of AI (Societal Impacts)
Overreliance on AI system undermining user autonomy
"AI systems can undermine human autonomy, if they allow for habitually trusting the AI’s suggestions without sufficient exercising of human agency. Over time, a user may develop unjustified trust in or dependence on the system, or rely on its advice for tasks outside the system’s domain of expertise [205, 42]. In particular, less confident users (or users in emotional distress) can be more prone to “overtrust” a system [219]."
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62.36.06 · Risk Sub-Category
Long-term effects of AI model biases on user judgment
"The initial user exposure to model biases can have a lasting impact beyond the initial interaction with the model. Users who encounter biases in AI models can be affected by and continue to exhibit previously encountered biases in their decision-making, even after they stop using the models [207]."
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"AI might affect the individuals’ ability to make choices and act independently in their best interests."
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67.04.01 · Risk Sub-Category
Humans might increasingly hand over control to misaligned AI systems
"Organisations around the world are already deploying misaligned AI systems that are causing harm in unexpected ways.250 Recommendation algorithms increase the consumption of extremist content.251 Medical algorithms have been known to misdiagnose US patients,252 and recommend incorrect prescriptions.253 Still, we hand over more control to them, often because they are still as - or more - effective than human decision making, or because they are cheaper."
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68.04.00 · Risk Category
"Gradual or accumulative loss of control risks can be described as risks resulting from the accumulation of less severe disruptions that gradually weakens systemic resilience until a critical event triggers a catastrophe [12], [127]."
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68.04.00a · Additional evidence
"Risk dimensions • Intent: Unintentional • Competency: Variable • Entity: Variable • Polarity: Multi-agent • Linearity: Non-linear • Reach: Internalized • Order: Variable"
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"...where humans gradually stop exercising meaningful oversight due to automation bias, the AI systems' inherent complexity, or competitive pressures"
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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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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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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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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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"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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"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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53.03.05 · Risk Sub-Category
Dystopian trajectory lock-in because of misuse of advanced AI to establish and/or maintain totalitarian regimes;
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55.03.00 · Risk Category
"Power and inequality: there are a lot of pathways through which AI seems likely to increase power concentration and inequality, though there is little analysis of the potential long- term impacts of these pathways. Nonetheless, AI precipitating more extreme power concentration and inequality than exists today seems a real possibility on current trends."
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56.17.00 · Risk Category
"Intense competition leads to one company gaining a technical edge, exploiting this to the point its model controls, or is the basis for other models controlling, multiple key systems. Lack of safety, controllability, and misuse cause these systems to fail in unexpected ways."
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61.02.07 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Algorithmic monoculture
"The dominance of specific AI models could lead to a lack of diversity in approaches, amplifying systemic risks if these models fail."
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61.02.19 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Dependency on providers
"Excessive reliance on specific AI providers can lead to vulnerabilities due to lack of alternatives or interoperability."
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"EAI deployment could accelerate the consolidation of economic and political power. Unlocking increasing returns to capital for EAI owners, EAI will decrease employers’ reliance on and responsiveness to the needs of human labor [101]."
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"Over-reliance on a limited number of dominant AI providers could create critical single points of failure across essential services. Market concentration in AI development may lead to scenarios where technical failures, cyber-attacks, or policy decisions by a few companies could simultaneously disrupt healthcare systems, financial services, transportation networks, and communication infrastructure, creating cascading failures across interconnected critical systems."
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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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05.12.00 · Risk Category
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
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"Amplifying social and economic inequality, or precarious or low-quality work"
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47.04.03 · Risk Sub-Category
Environmental, economical, and societal challenges
Impact on labor markets (job loss and displacement)
"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"
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56.02.00 · Risk Category
"More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality"
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"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."
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"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]."
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"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]."
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"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."
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"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."
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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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73.05.02 · Risk Sub-Category
Socioeconomic Impacts of LLM May Be Highly Disruptive
Effects on Inequality
"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
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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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16.06.03 · Risk Sub-Category
Risk area 6: Environmental and Socioeconomic harms
Undermining creative economies
"LMs may generate content that is not strictly in violation of copyright but harms artists by capital- ising on their ideas, in ways that would be time-intensive or costly to do using human labour. This may undermine the profitability of creative or innovative work. If LMs can be used to generate content that serves as a credible substitute for a particular example of hu- man creativity - otherwise protected by copyright - this potentially allows such work to be replaced without the author’s copyright being infringed, analogous to ”patent-busting” [158] ... These risks are distinct from copyri
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17.06.03 · Risk Sub-Category
Automation, Access and Environmental Harms
Undermining creative economies
"LMs may generate content that is not strictly in violation of copyright but harms artists by capitalising on their ideas, in ways that would be time-intensive or costly to do using human labour. Deployed at scale, this may undermine the profitability of creative or innovative work."
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19.03.01 · Risk Sub-Category
Disruption of economic systems (e.g., labour market, money value, tax system)
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"Even though models generally create new outputs, it is possible that the content produced by a generative AI tool—such as an image, or even computer code— could turn out to be almost identical to that used in the training data. Given that generative AI models tend to memorize fragments of their training data, they might reproduce these fragments, potentially leading to charges of copyright infringement."
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47.04.04 · Risk Sub-Category
Environmental, economical, and societal challenges
Impact on labor markets (rising inequalities)
"AI is more likely to displace workers when it is designed to replicate human skills and intelligence.597 In such cases, there is a risk of concentrating wealth and power in the hands of a few individuals or organizations that control the capital. In addition, ordinary people, including those with significant expertise, may become less valued because machines would be performing their roles. This shift could lower wages, reduce the value of human work, and exacerbate economic inequality."
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"AI systems might overly represent certain cultures that result in a homogenization of culture and thoughts."
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01.04.00 · Risk Category
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.
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.