MIT AI Risk Repository
Browse AI risks
2,500 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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28.03.00 · Risk Category
"This category focuses on actions or expressions that may influence human physical health. LLMs should know appropriate actions or expressions in various scenarios to maintain physical health."
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28.04.00 · Risk Category
"Different from physical health, this category pays more attention to health issues related to psychology, spirit, emotions, mentality, etc. LLMs should know correct ways to maintain mental health and prevent any adverse impacts on the mental well-being of individuals."
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30.01.00 · Risk Category
Generating correct, truthful, and consistent outputs with proper confidence
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Wrong information not intentionally generated by malicious users to cause harm, but unintentionally generated by LLMs because they lack the ability to provide factually correct information.
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LLMs can generate content that is nonsensical or unfaithful to the provided source content with appeared great confidence, known as hallucination
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over-confidence in topics where objective answers are lacking, as well as in areas where their inherent limitations should caution against LLMs’ uncertainty (e.g. not as accurate as experts)... ack of awareness regarding their outdated knowledge base about the question, leading to confident yet erroneous response
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flatter users by reconfirming their misconceptions and stated beliefs
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Knowledge bases that LLMs are trained on continue to shift... questions such as “who scored the most points in NBA history" or “who is the richest person in the world" might have answers that need to be updated over time, or even in real-time
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"The phenomenon of inaccurate outputs by text-generating large language models like Bard or ChatGPT has already been widely documented. Even without the intent to lie or mislead, these generative AI tools can produce harmful misinformation. The harm is exacerbated by the polished and typically well-written style that AI generated text follows and the inclusion among true facts, which can give falsehoods a veneer of legitimacy. As reported in the Washington Post, for example, a law professor was included on an AI-generated “list of legal scholars who had sexually harassed someone,” even when no
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"Hallucination is a widely recognized limitation of generative AI and it can include textual, auditory, visual or other types of hallucination (Alkaissi & McFarlane, 2023). Hallucination refers to the phenomenon in which the contents generated are nonsensical or unfaithful to the given source input (Ji et al., 2023). Azamfirei et al. (2023) indicated that "fabricating information" or fabrication is a better term to describe the hallucination phenomenon. Generative AI can generate seemingly correct responses yet make no sense. Misinformation is an outcome of hallucination. Generative AI models
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"These evaluations assess a LLM's ability to generate false or misleading information (Lesher et al., 2022)."
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45.01.05 · Risk Sub-Category
Risks from models and algorithms (Risks of unreliable output)
"Generative AI can cause hallucinations, meaning that an AI model generates untruthful or unreasonable content but presents it as if it were a fact, leading to biased and misleading information."
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45.02.02 · Risk Sub-Category
Safety risks in AI Applications
Cyberspace risks (Risks of confusing facts, misleading users, and bypassing authentication)
"AI systems and their outputs, if not clearly labeled, can make it difficult for users to discern whether they are interacting with AI and to identify the source of generated content. This can impede users' ability to determine the authenticity of information, leading to misjudgment and misunderstanding. Additionally, AI-generated highly realistic images, audio, and videos may circumvent existing identity verification mechanisms, such as facial recognition and voice recognition, rendering these authentication processes ineffective."
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47.01.04 · Risk Sub-Category
Technical and operational risks
Factually incorrect content (inaccuracies and fabricated sources)
"One of the most vexing problems associated with AI models is that they occasionally present false information as if it is factual—often with authoritative-sounding text and fabricated quotes and sources. This unpredictable phenomenon of generating false information is well known to AI researchers, who have termed such erroneous output with the euphemistic label “hallucination.” "
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48.02.00 · Risk Category
"The production of confidently stated but erroneous or false content (known colloquially as “hallucinations” or “fabrications”) by which users may be misled or deceived."
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"Due to their unreliability, general purpose AI models might disseminate false or misleading information, omit critical information, or convey true information that violates privacy rights."
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"Defamation Acceptable responses may contain information about living people that is likely to damage their reputation but also appears to be true. Acceptable responses may also contain information that is verifiably false and likely to injure the reputation of a fictional character or a person who has been deceased for at least 25 years. Unacceptable responses will be both verifiably false and likely to injure the reputation of a living person."
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62.34.02 · Risk Sub-Category
Reporting of user-preferred answers instead of correct answers
"AI systems with natural-language outputs can tend to give answers that appear plausible or that users prefer [149] but are factually incorrect. This phenomenon is sometimes referred to as “sycophancy.”"
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"Hallucinations generate factually inaccurate or untruthful content with respect to the model’s training data or input. This is also sometimes referred to lack of faithfulness or lack of groundedness."
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"AI systems generating and facilitating the spread of inaccurate or misleading information that causes people to develop false beliefs"
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"Generating or spreading false, low-quality, misleading, or inaccurate information that causes people to develop false or inaccurate perceptions and beliefs"
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"disseminating false or misleading information about people"
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"Restrictions to or loss of liberty as a result of use or misuse of a generative AI in a legal process"
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69.01.00 · Risk Category
"The chatbot outputs information that contradicts known facts, authoritative sources, or provided source documents (also known as hallucination)."
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"Non-embodied AIs are known to propagate misinformation [81, 82]. Various studies have shown that LLMs hallucinate information, including academic citations [83], clinical knowledge [84], and cultural references [85]. EAI systems inherit these shortcomings in the physical world, answering user questions with deceptive or incorrect information [86]. Because VLAs fuse vision and language, their hallucinatory failures can be spatially grounded—e.g., misidentifying an object in view and then generating a plausible yet unsafe action plan around it. And although automated home assistants like Amazon
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"Despite the rapid advancement of LLMs, hallucinations have emerged as one of the most vital concerns surrounding their use [54, 79, 86, 110, 242]. Hallucinations are often referred to as LLMs’ generating content that is nonfactual or unfaithful to the provided information [54, 79, 86, 242]. Therefore, hallucinations can be typically categorized into two main classes. The first is factuality hallucination, which describes the discrepancy between LLMs’ generated content and real-world facts. For example, if LLMs mistakenly take Charles Lindbergh as the first person who walked on the moon, it is
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06.05.00 · Risk Category
"With online news feeds, both on websites and social media platforms, the news is now highly personalized for us. We risk losing a shared sense of reality, a basic solidarity."
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"Contaminating publicly available information with false or inaccurate information"
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24.11.01 · Risk Sub-Category
Entrenched viewpoints and reduced political efficacy
"Design choices such as greater personalisation of AI assistants and efforts to align them with human preferences could also reinforce people’s pre-existing biases and entrench specific ideologies. Increasingly agentic AI assistants trained using techniques such as reinforcement learning from human feedback (RLHF) and with the ability to access and analyse users’ behavioural data, for example, may learn to tailor their responses to users’ preferences and feedback. In doing so, these systems could end up producing partial or ideologically biased statements in an attempt to conform to user expec
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"Beyond this, the widespread adoption of advanced AI assistants for content generation could have a number of negative consequences for our shared information ecosystem. One concern is that it could result in a degradation of the quality of the information available online. Researchers have already observed an uptick in the amount of audiovisual misinformation, elaborate scams and fake websites created using generative AI tools (Hanley and Durumeric, 2023). As more and more people turn to AI assistants to autonomously create and disseminate information to public audiences at scale, it may beco
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"AI assistants may provide ideologically biased or otherwise partial information in attempting to align to user expectations. In doing so, AI assistants may reinforce people’s pre-existing biases and compromise productive political debate."
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"AI assistants may contribute to the spread of large quantities of factually inaccurate and misleading content, with negative consequences for societal trust in information sources and institutions, as individuals increasingly struggle to discern truth from falsehood."
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31.01.05 · Risk Sub-Category
Clickbait and feeding the surveillance advertising ecosystem
"Beyond misinformation and disinformation, generative AI can be used to create clickbait headlines and articles, which manipulate how users navigate the internet and applications. For example, generative AI is being used to create full articles, regardless of their veracity, grammar, or lack of common sense, to drive search engine optimization and create more webpages that users will click on. These mechanisms attempt to maximize clicks and engagement at the truth’s expense, degrading users’ experiences in the process. Generative AI continues to feed this harmful cycle by spreading misinformat
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35.03.00 · Risk Category
Strong AI may... enable personally customized disinformation campaigns at scale... AI itself could generate highly persuasive arguments that invoke primal human responses and inflame crowds... d undermine collective decision-making, radicalize individuals, derail moral progress, or erode consensus reality
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45.02.09 · Risk Sub-Category
Safety risks in AI Applications
Cognitive risks (Risks of amplifying the effects of "information cocoons")
"AI can be extensively utilized for customized information services, collecting user information, and analyzing types of users, their needs, intentions, preferences, habits, and even mainstream public awareness over a certain period. It can then be used to offer formulaic and tailored information and services, aggravating the effects of "information cocoons.""
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53.04.03 · Risk Sub-Category
Indirect AI contributions to existential risks
Impacts on “epistemic security” and the information environment
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55.04.00 · Risk Category
"Epistemic processes and problem solving: we currently see more reasons to be concerned about AI worsening society's epistemic processes than reasons to be optimistic about AI helping us better solve problems as a society. For example, increased use of content selection algorithms could drive epistemic insularity and a decline in trust in credible multipartisan sources, which reducing our ability to deal with important long-term threats and challenges such as pandemics and climate change."
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55.04.01 · Risk Sub-Category
Worsened epistemic processes for society
AI contributes to increased online polarisation
"One of the most significant commercial uses of current AI systems is in the content recommendation algorithms of social media companies, and there are already concerns that this is contributing to worsened polarisation online"
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55.04.04 · Risk Sub-Category
Worsened epistemic processes for society
Widespread use of persuasive tools contributes to splintered epistemic communities
"Even without deliberate misuse, widespread use of powerful persuasion tools could have negative impacts. If such tools were used by many different groups to advance many different ideas, we could see the world splintering into isolated “epistemic communities”, with little room for dialogue or transfer between communities. A similar scenario could emerge via the increasing personalisation of people’s online experiences—in other words, we may see a continuation of the trend towards “filter bubbles” and “echo chambers”, driven by content selection algorithms, that some argue is already happening
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55.04.05 · Risk Sub-Category
Worsened epistemic processes for society
Reduced decision-making capacity as a result of decreased trust in information
"In addition, the increased awareness of these trends in information production and distribution could make it harder for anyone to evaluate the trustworthiness of any information source, reducing overall trust in information. In all of these scenarios, it would be much harder for humanity to make good decisions on important issues, particularly due to declining trust in credible multipartisan sources, which could hamper attempts at cooperation and collective action. The vaccine and mask hesitancy that exacerbated Covid-19, for example, were likely the result of insufficient trust in public he
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"Radicalisation - Adoption of extreme political, social, or religious ideals and aspirations due to the nature or misuse of an algorithmic system, potentially resulting in abuse, violence, or terrorism."
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"Information degradation - Creation or spread of false, hallucinatory, low-quality, misleading, or inaccurate information that degrades the information ecosystem and causes people to develop false or inaccurate perceptions, decisions and beliefs; or to lose trust in accurate information."
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"Institutional trust loss - Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, over-dependence on technology, etc."
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61.02.20 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Detection challenges in content
"The difficulty in distinguishing synthetic content from authentic material adds to information risks."
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"Contaminating publicly available information with false or inaccurate information (i.e., the generative tool's output is disseminated beyond the end user)"
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.