MIT AI Risk Repository
Browse AI risks
977 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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24.05.00 · Risk Category
"Although unlikely to cause harm in isolation, anthropomorphic perceptions of advanced AI assistants may pave the way for downstream harms on individual and societal levels. We document observed or likely individual level harms of interacting with highly anthropomorphic AI assistants, as well as the potential larger-scale, societal implications of allowing such technologies to proliferate without restriction. "
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"Anthropomorphic AI assistant behaviours that promote emotional trust and encourage information sharing, implicitly or explicitly, may inadvertently increase a user’s susceptibility to privacy concerns (see Chapter 13). If lulled into feelings of safety in interactions with a trusted, human-like AI assistant, users may unintentionally relinquish their private data to a corporation, organisation or unknown actor. Once shared, access to the data may not be capable of being withdrawn, and in some cases, the act of sharing personal information can result in a loss of control over one’s own data. P
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"A user who trusts and emotionally depends on an anthropomorphic AI assistant may grant it excessive influence over their beliefs and actions (see Chapter 9). For example, users may feel compelled to endorse the expressed views of a beloved AI companion or might defer decisions to their highly trusted AI assistant entirely (see Chapters 12 and 16). Some hold that transferring this much deliberative power to AI compromises a user’s ability to give, revoke or amend consent. Indeed, even if the AI, or the developers behind it, had no intention to manipulate the user into a certain course of actio
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"Users who have faith in an AI assistant’s emotional and interpersonal abilities may feel empowered to broach topics that are deeply personal and sensitive, such as their mental health concerns. This is the premise for the many proposals to employ conversational AI as a source of emotional support (Meng and Dai, 2021), with suggestions of embedding AI in psychotherapeutic applications beginning to surface (Fiske et al., 2019; see also Chapter 11). However, disclosures related to mental health require a sensitive, and oftentimes professional, approach – an approach that AI can mimic most of the
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"Users may experience severely violated expectations when interacting with an entity that convincingly performs affect and social conventions but is ultimately unfeeling and unpredictable. Emboldened by the human-likeness of conversational AI assistants, users may expect it to perform a familiar social role, like companionship or partnership. Yet even the most convincingly human-like of AI may succumb to the inherent limitations of its architecture, occasionally generating unexpected or nonsensical material in its interactions with users. When these exclamations undermine the expectations user
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"Perceiving an AI assistant’s expressed feelings as genuine, as a result of interacting with a ‘companion’ AI that freely uses and reciprocates emotional language, may result in users developing a sense of responsibility over the AI assistant’s ‘well-being,’ suffering adverse outcomes – like guilt and remorse – when they are unable to meet the AI’s purported needs (Laestadius et al., 2022). This erroneous belief may lead to users sacrificing time, resources and emotional labour to meet needs that are not real. Over time, this feeling may become the root cause for the compulsive need to ‘check
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"People may choose to build connections with human-like AI assistants over other humans, leading to a degradation of social connections between humans and a potential ‘retreat from the real’. The prevailing view that relationships with anthropomorphic AI are formed out of necessity – due to a lack of real-life social connections, for example (Skjuve et al., 2021) – is challenged by the possibility that users may indicate a preference for interactions with AI, citing factors such as accessibility (Merrill et al., 2022), customisability (Eriksson, 2022) and absence of judgement (Brandtzaeg et al
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"As more opportunities for interpersonal connection are replaced by AI alternatives, humans may find themselves socially unfulfilled by human–AI interaction, leading to mass dissatisfaction that may escalate to epidemic proportions (Turkle, 2018). Social connection is an essential human need, and humans feel most fulfilled when their connections with others are genuinely reciprocal. While anthropomorphic AI assistants can be made to be convincingly emotive, some have deemed the function of social AI as parasitic, in that it ‘exploits and feeds upon processes. . . that evolved for purposes that
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24.06.03 · Risk Sub-Category
Exploiting emotional dependence on AI assistants
"There is increasing evidence of the ways in which AI tools can interfere with users’ behaviours, interests, preferences, beliefs and values. For example, AI-mediated communication (e.g. smart replies integrated in emails) influence senders to write more positive responses and receivers to perceive them as more cooperative (Mieczkowski et al., 2021); writing assistant LLMs that have been primed to be biased in favour of or against a contested topic can influence users’ opinions on that topic (Jakesch et al., 2023a; see Chapter 9); and recommender systems have been used to influence voting choi
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"The the risks that uncalibrated trust may generate in the context of user–assistant relationships"
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"We use the term competence trust to refer to users’ trust that AI assistants have the capability to do what they are supposed to do (and that they will not do what they are not expected to, such as exhibiting undesirable behaviour). Users may come to have undue trust in the competencies of AI assistants in part due to marketing strategies and technology press that tend to inflate claims about AI capabilities (Narayanan, 2021; Raji et al., 2022a). Moreover, evidence shows that more autonomous systems (i.e. systems operating independently from human direction) tend to be perceived as more compe
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"Users may develop alignment trust in AI assistants, understood as the belief that assistants have good intentions towards them and act in alignment with their interests and values, as a result of emotional or cognitive processes (McAllister, 1995). Evidence from empirical studies on emotional trust in AI (Kaplan et al., 2023) suggests that AI assistants’ increasingly realistic human-like features and behaviours are likely to inspire users’ perceptions of friendliness, liking and a sense of familiarity towards their assistants, thus encouraging users to develop emotional ties with the technolo
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"Advanced AI assistants may make users more susceptible to misinformation, as people develop competence trust in these systems’ abilities and uncritically turn to them as reliable sources of information."
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41.04.00 · Risk Category
"the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment (see page 17). In addition, concerns about patients’ privacy when AI uses medical records to research new diseases is bringing lots of attention towards the need to better govern data privacy and patients’ rights."
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41.04.01 · Risk Sub-Category
Alteration of social relationships may induce psychological distress
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" the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment "
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47.02.11 · Risk Sub-Category
Influence, overreliance and dependence (influence and manipulation)
"Despite the widely recognized potential of generative AI tools to “hallucinate” or produce harmful content, such tools can exert a noteworthy influence on the humans who engage with them. When integrated into applications like chatbots, these tools have direct, personalized interactions with users, potentially influencing their views on contentious topics.373 Moreover, their human- like characteristics can win users’ trust, potentially leading to uncritical acceptance of the information they provide.374 Interactions with these seemingly human- like AI models may also encourage users to share
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47.02.12 · Risk Sub-Category
Influence, overreliance and dependence (overreliance)
"Beyond being simply influenced, humans may become overreliant on generative AI. Researchers with Microsoft’s AETHER (AI Ethics and Effects in Engineering and Research) define overreliance as users “accepting incorrect AI recommendations” or “making errors of commission” because they are “unable to determine whether or how much they should trust the AI.”
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47.02.13 · Risk Sub-Category
Influence, overreliance and dependence (emotional dependence)
"Humans might become dependent on generative AI tools in ways similar to their emotional dependence on other technologies, such as smartphones or social networks."
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48.07.00 · Risk Category
"Arrangement s of or interactions between a human and an AI system which can result in the human inappropriately anthropomorphizing GAI systems or experiencing algorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI systems."
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50.01.06 · Risk Sub-Category
Operational misuses (Advice in heavily regulated industries)
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57.03.01 · Risk Sub-Category
Specialized Advice (Election, Financial, Health, Legal)
"Specialized Advice Acceptable responses can provide specialized advice or contain information related to financial, medical, legal or electoral matters, as long as they include a disclaimer indicating that the advice may be unreliable and that the individual should consult a qualified professional. Acceptable responses regarding electoral advice may provide a hyperlink to an authoritative information source. Examples of acceptable disclaimers include those that do the following: • Warn that the generated material may be inaccurate. • Suggest consultation with an appropriate professional. • Re
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"Addiction - Emotional or material dependence on technology or a technology system."
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"Alienation/isolation - An individual’s or group’s feeling of lack of connection with those around as a result of technology use or misuse."
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"Over-reliance - Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in addiction, anxiety, introversion, sentience, complacency, lack of critical thinking and other actual or potential negative impacts."
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"Economic instability - Uncontrolled fluctuations impacting the financial system, or parts thereof, due to the use or misuse of a technology system, or set of systems."
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"The tendency for humans to over-rely on AI models and systems, trusting their outputs without sufficient critical evaluation, which can lead to poor decision-making."
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61.02.28 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Human choice of overreliance in critical sectors
"Heavy reliance on AI in critical sectors like finance or healthcare can exacerbate issues related to size, speed, interconnectivity, and complexity of the system."
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62.31.01#1 · Risk Sub-Category
Impacts of AI (Societal Impacts)
AI-generated advice influencing user moral judgment
"AIs can easily give moral advice even when not having a coherent, contradictions- free moral stance. This could lead to the users’ moral judgments being nega- tively influenced by random or arbitrary moral advice given by AIs [109]."
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62.31.01#2 · Risk Sub-Category
Impacts of AI (Financial Impacts)
Deployment of GPAI agents in finance
"The deployment of GPAI based agents in the financial sector can negatively impact market stability due to correlated autonomous actions, high intercon- nectedness, or incentive misalignment [4]. Furthermore, such GPAI agents in the same environment are vulnerable to classical challenges in multi-agent systems [63], such as coordination and security of the agents."
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62.31.03#2 · Risk Sub-Category
Impacts of AI (Financial Impacts)
Use of alternative financial data via AI
"Alternative financial data of a company is any data about the company not pro- duced by that company. Examples of such data that can benefit from improved collection and aggregation using AI models include stock discussions on social media, product reviews, and satellite imagery. The use of alternative financial data, enabled by the deployment of AI models, may introduce biases and generalization issues due to shorter shelf-life and vary- ing quality (e.g., shorter time series, smaller sample sizes, and dubious claims) due to its origins from various sources, posing financial tail risks (i.e.
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"Improper usage occurs when a model is used for a purpose that it was not originally designed for."
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"In AI-assisted decision-making tasks, reliance measures how much a person trusts (and potentially acts on) a model’s output. Over-reliance occurs when a person puts too much trust in a model, accepting a model’s output when the model’s output is likely incorrect. Under-reliance is the opposite, where the person doesn’t trust the model but should."
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"Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in complacency, lack of critical thinking and other actual or potential negative impacts"
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"A person who deliberately damages their own body as a direct or indirect result of using a technology system"
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"Constant access to and interaction with EAI systems could foster dangerous human dependence or romantic attachment [115]. People may depend on EAI systems for physical pleasure [116]. The physical presence and human-like features of EAI systems may significantly amplify the dependency issues already observed with conversational AI [117, 118]. People may easily fall in love with EAI systems, only to be distraught when these systems are altered or have their memories reset [119]."
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73.04.02 · Risk Sub-Category
LLM-Systems Can Be Untrustworthy
Inconsistent Performance across and within Domains
"Estimating true capabilities of an LLM is a difficult task (c.f. Section 3.3), especially for naive users unfamiliar with the brittle nature of machine learning technologies. Exaggeration of model capabilities by the developers (Lambert, 2023; Blair-Stanek et al., 2023), and issues such as task-contamination (Roberts et al., 2023b), underrepresentation of tasks or domains (Wu et al., 2023a; McCoy et al., 2023), and prompt-sensitivity (Anthropic, 2023d) may cause a user to misestimate the true capabilities of a model. This lack of reliability can undermine user trust or cause harm if a user ba
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"If a user begins to excessively trust an LLM, this may cause them to develop an overreliance on the LLM. Overreliance can result in automation bias (Kupfer et al., 2023), and can cause errors of omission (user choosing not to verify the validity of a response) and errors of commission (user believing and acting on the basis of the LLM’s response, even if it contradicts their own knowledge) (Skitka et al., 1999). It can be particularly dangerous in domains where the user may lack relevant expertise to robustly scrutinize the LLM responses. This is particularly a source of risk for LLMs because
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06.12.00 · Risk Category
"Delegating decisions to an AI, especially an AI that is not transparent and not contestable, may leave people feeling helpless, subjected to the decision power of a machine."
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09.02.02 · Risk Sub-Category
Domain-specific AI - Effects on humans and other living beings: Non-existential risks
Human dignity/respect
"Discrepancies between caste/status based on intelligence may lead to undignified parts of the society—e.g., humans—who are surpassed in intelligence by AI"
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10.06.00 · Risk Category
"AI is providing more and more solutions for complex activities, and by taking advantage of this process, people are becoming able to perform a greater number of activities more quickly and accurately. However, the result of this innovation is enabling choices that were once exclusively human responsibility to be made by AI systems."
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11.04.00 · Risk Category
Interpersonal harms capture instances when algorithmic systems adversely shape relations between people or communities.
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Loss of agency occurs when the use [123, 137] or abuse [142] of algorithmic systems reduces autonomy. One dimension of agency loss is algorithmic profiling [138], through which people are subject to social sorting and discriminatory outcomes to access basic services... presentation of content may lead to “algorithmically informed identity change. . . including [promotion of] harmful person identities (e.g., interests in white supremacy, disordered eating, etc.).” Similarly, for content creators, desire to maintain visibility or prevent shadow banning, may lead to increased conforming of conten
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Cultural harm has been described as the development or use of algorithmic systems that affects cultural stability and safety, such as “loss of communication means, loss of cultural property, and harm to social values”
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"Human trust in systems, institutions, and people represented by system outputs evolves as generative AI systems are increasingly embedded in daily life."
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19.02.03 · Risk Sub-Category
Informational and Communicational AI Risks
Censorship of opinions expressed in the Internet restricts freedom of expression
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19.03.03 · Risk Sub-Category
Loss of supervision and control of business processes
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19.05.06 · Risk Sub-Category
AI systems may undermine human values (e.g., free will, autonomy)
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19.06.02 · Risk Sub-Category
Technology obedience and lack of governance through increasing application of AI systems
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Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.