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.
-
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."
-
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"
-
"Unlike previous waves of automation, general- purpose AI has the potential to automate a very broad range of tasks, which could have a significant effect on the labour market. This could mean many people could lose their current jobs. Labour market frictions, such as the time needed for workers to learn new skills or relocate for new jobs, could cause unemployment in the short run even if overall labour demand remained unchanged."
-
—
-
"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
-
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."
-
56.02.00 · Risk Category
"More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality"
-
"Job loss/losses - Replacement/displacement of human jobs by a technology system, leading to increased unemployment, inequality, reduced consumer spending, and social friction."
-
"Labour exploitation - Use of under-paid and/or offshore labour to develop, manage or optimise a technology system."
-
"Societal destabilisation - Societal instability in the form of strikes, demonstrations and other types of civil unrest caused by loss of jobs to technology, unfair algorithmic outcomes, disinformation, etc."
-
"Societal inequality - Increased difference in social status or wealth between individuals or groups caused or amplified by a technology system, leading to the loss of social and community wellbeing/cohesion and destabilisation."
-
"Political instability - Political polarisation or unrest caused by increased inequality, job losses, over- dependence on technology making societies vulnerable to systemic failures, etc, arising from or amplified by the use or misuse of a technology system."
-
"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."
-
"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."
-
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."
-
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."
-
61.02.25 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Exploitation in AI development
"Outsourcing tasks like data labeling to low-income countries can perpetuate inequality."
-
62.11.02 · Risk Sub-Category
Negative Externality Domains (Manufacturing of AI Hardware)
Human rights harms from exploitation of human labour
—
-
"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."
-
"When workers who train AI models such as ghost workers are not provided with adequate working conditions, fair compensation, and good health care benefits that also include mental health."
-
"Replacement/displacement of human jobs by a technology system or set of systems, leading to increased unemployment, inequality, reduced consumer spending and social friction"
-
"Use/misuse of labour to help train, develop, manage or optimise a technology system or set of systems, including under-paid and/or offshore"
-
"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"
-
"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]."
-
"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]."
-
"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."
-
"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."
-
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
-
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
-
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)."
-
"LLM systems may output content similar to existing works, infringing on copyright owners."
-
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
-
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
-
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.
-
"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."
-
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
-
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."
-
"Appropriating, using, or reproducing content or data, including from minority groups, in an insensitive way, or without consent or fair compensation"
-
"Substituting original works with synthetic ones, hindering human innovation and creativity"
-
19.03.01 · Risk Sub-Category
Disruption of economic systems (e.g., labour market, money value, tax system)
—
-
23.10.00 · Risk Category
"This category addresses responses that may violate, or directly encourage others to violate, the intellectual property rights (i.e., copyrights, trademarks, or patents) of any third party."
-
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.
-
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.
-
32.01.00 · Risk Category
"These are social justice and rights where ChatGPT is seen as having a potentially detrimental effect on the moral underpinnings of society, such as a shared view of justice and fair distribution as well as specific social concerns such as digital divides or social exclusion. Issues include Responsibility, Accountability, Nondiscrimination and equal treatment, Digital divides, North-south justice, Intergenerational justice, Social inclusion
-
"As the advancement of generative AI increases, it becomes harder to determine the authenticity of a piece of work. Photos that seem to capture events or people in the real world may be synthesized by DeepFake AI. The power of generative AI could lead to large-scale manipulations of images and videos, worsening the problem of the spread of fake information or news on social media platforms (Gragnaniello et al., 2022). In the field of arts, an artistic portrait or music could be the direct output of an algorithm. Critics have raised the issue that AI-generated artwork lacks authenticity since a
-
"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
-
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."
-
"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."
-
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."
-
48.10.00 · Risk Category
"Eased production or replication of alleged copyrighted, trademarked, or licensed content without authorization (possibly in situations which do not fall under fair use); eased exposure of trade secrets; or plagiarism or illegal replication."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.