MIT AI Risk Repository
Browse AI risks
543 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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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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"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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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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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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"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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"Researchers in deep learning and those with greater research impact are more likely to migrate to industry, raising concerns about the “privatization of AI knowledge” [278]. Specically, if the most sophisticated AI approaches become proprietary and are used only within private research labs, then it will be impossible for universities to teach them, let alone contribute to leading research."
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55.03.01 · Risk Sub-Category
Increased power concentration and inequality
Unequal distribution of harms and benefits
"AI-driven industries seem likely to tend towards monopoly and could result in huge economic gains for a few actors: there seems to be a feedback loop whereby actors with access to more AI-relevant resources (e.g., data, computing power, talent) are able to build more effective digital products and services, claim a greater market share, and therefore be well-positioned to amass more of the relevant resources [14, 39, 45]. Similarly, wealthier countries able to invest more in AI development are likely to reap economic benefits more quickly than developing economies, potentially widening the ga
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"Monopolisation - Abuse of market power through the control of prices, thereby limiting competition and creating unfair barriers to entry."
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"The concentration of military, economic, or political power of entities in possession or control of AI or AI-enabled technologies."
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09.02.06 · Risk Sub-Category
Domain-specific AI - Effects on humans and other living beings: Non-existential risks
Inequality of wealth
"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"
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09.03.01 · Risk Sub-Category
AGI - Effects on humans and other living beings: Existential risks
Direct competition with humans
"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."
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10.04.00 · Risk Category
"Eliminated jobs in various types of companies."
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"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
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18.06.05 · Risk Sub-Category
Socioeconomic and environmental harms
Exploitative data sourcing and enrichment
"Perpetuating exploitative labour practices to build AI systems (sourcing, user testing)"
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19.03.02 · Risk Sub-Category
Replacement of humans and unemployment due to AI automation
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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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31.07.00 · 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. 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
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31.07.02 · Risk Sub-Category
Labor Manipulation, Theft, and Displacement
Job Automation Instead of Augmentation
"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
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"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,
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"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."
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33.04.03 · Risk Sub-Category
Challenges associated with the economy:
Income inequality and monopolies
"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
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"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"
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45.02.11 · Risk Sub-Category
Safety risks in AI Applications
Ethical Risks (Risks of exacerbating social discrimination and prejudice, and widening the intelligence divide)
"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."
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55.03.02 · Risk Sub-Category
Increased power concentration and inequality
AI-based automation increases income inequality
"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."
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"Labour exploitation - Use of under-paid and/or offshore labour to develop, manage or optimise a technology system."
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61.02.01 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Ability to automate jobs
"The ability to automate jobs by AI models and systems can lead to significant job displacement, economic disruption, and social inequality."
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61.02.10 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Capabilities that enable substitution of humans
"The progressive replacement of human roles by AI models and systems can lead to societal disruption."
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"Use/misuse of labour to help train, develop, manage or optimise a technology system or set of systems, including under-paid and/or offshore"
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"LLM systems may output content similar to existing works, infringing on copyright owners."
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05.16.00 · Risk Category
In this cluster, concerns about negative impacts on human creativity, particularly through text-to-image models, are prevalent. Papers criticize financial harms or economic losses for artists due to the widespread generation of synthetic art as well as the unauthorized and uncompensated use of artists' works in training datasets. Additionally, given the challenge of distinguishing synthetic images from authentic ones, there is a call for systematically disclosing the non-human origin of such content, particularly through watermarking. Moreover, while some sources argue that text-to-image model
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05.17.00 · Risk Category
The emergence of generative AI raises issues regarding disruptions to existing copyright norms. Frequently discussed in the literature are violations of copyright and intellectual property rights stemming from the unauthorized collection of text or image training data. Another concern relates to generative models memorizing or plagiarizing copyrighted content. Additionally, there are open questions and debates around the copyright or ownership of model outputs, the protection of creative prompts, and the general blurring of traditional concepts of authorship.
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"Economic incentives to augment and not automate human labor, thought, and creativity should examine the ongoing effects generative AI systems have on skills, jobs, and the labor market."
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"Appropriating, using, or reproducing content or data, including from minority groups, in an insensitive way, or without consent or fair compensation"
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The memorization effect of LLM on training data can enable users to extract certain copyright-protected content that belongs to the LLM’s training data.
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31.05.00 · Risk Category
"The extent and effectiveness of legal protections for intellectual property have been thrown into question with the rise of generative AI. Generative AI trains itself on vast pools of data that often include IP-protected works.
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"According to the U.S. Copyright Office (n.d..), copyright is "a type of intellectual property that protects original works of authorship as soon as an author fixes the work in a tangible form of expression" (U.S. Copyright Office, n.d..). Generative AI is designed to generate content based on the input given to it. Some of the contents generated by AI may be others' original works that are protected by copyright laws and regulations. Therefore, users need to be careful and ensure that generative AI has been used in a legal manner such that the content that it generates does not violate copyri
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47.03.03 · Risk Sub-Category
Copyright challenges (training models using copyrighted output)
"Generative AI companies are regularly accused of violating copyright law by training AI models on copyrighted works without gaining permission or paying compensation to the copyright owners. In fact, a substantial number of copyrighted documents and books have been incorporated into the training datasets of generative AI models."
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"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."
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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"
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22.02.00 · Risk Category
"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."
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"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.”
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45.01.13 · Risk Sub-Category
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."
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"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."
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Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.