{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"24.04.04","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"AI Influence","risk_subcategory":"Sociocultural and Political Harms","description":"\"These harms interfere with the peaceful organisation of social life, including in the cultural and political spheres. AI assistants may cause or contribute to friction in human relationships either directly, through convincing a user to end certain valuable relationships, or indirectly due to a loss of interpersonal trust due to an increased dependency on assistants. At the societal level, the spread of misinformation by AI assistants could lead to erasure of collective cultural knowledge. In the political domain, more advanced AI assistants could potentially manipulate voters by prompting th","entity":"AI","intent":"Other","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.04.05","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"AI Influence","risk_subcategory":"Self-Actualisation Harms","description":"\"These harms hinder a person’s ability to pursue a personally fulfilling life. At the individual level, an AI assistant may, through manipulation, cause users to lose control over their future life trajectory. Over time, subtle behavioural shifts can accumulate, leading to significant changes in an individual’s life that may be viewed as problematic. AI systems often seek to understand user preferences to enhance service delivery. However, when continuous optimisation is employed in these systems, it can become challenging to discern whether the system is genuinely learning from user preferenc","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.05.07","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Anthropomorphism","risk_subcategory":"Disorientation","description":"\"Given the capacity to fine-tune on individual preferences and to learn from users, personal AI assistants could fully inhabit the users’ opinion space and only say what is pleasing to the user; an ill that some researchers call ‘sycophancy’ (Park et al., 2023a) or the ‘yea-sayer effect’ (Dinan et al., 2021). A related phenomenon has been observed in automated recommender systems, where consistently presenting users with content that affirms their existing views is thought to encourage the formation and consolidation of narrow beliefs (Du, 2023; Grandinetti and Bruinsma, 2023; see also Chapter","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.06.00","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Category","risk_category":"Appropriate Relationships","risk_subcategory":null,"description":"\"We anticipate that relationships between users and advanced AI assistants will have several features that are liable to give rise to risks of harm.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.06.02","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Appropriate Relationships","risk_subcategory":"Limiting users’ opportunities for personal development and growth","description":"some users look to establish relationships with their AI companions that are free from the hurdles that, in human relationships, derive from dealing with others who have their own opinions, preferences and flaws that may conflict with ours. \"AI assistants are likely to incentivise these kinds of ‘frictionless’ relationships (Vallor, 2016) by design if they are developed to optimise for engagement and to be highly personalisable. They may also do so because of accidental undesirable properties of the models that power them, such as sycophancy in large language models (LLMs), that is, the tenden","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.06.04","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Appropriate Relationships","risk_subcategory":"Generating material dependence without adequate commitment to user needs","description":"\"In addition to emotional dependence, user–AI assistant relationships may give rise to material dependence if the relationships are not just emotionally difficult but also materially costly to exit. For example, a visually impaired user may decide not to register for a healthcare assistance programme to support navigation in cities on the grounds that their AI assistant can perform the relevant navigation functions and will continue to operate into the future. Cases like these may be ethically problematic if the user’s dependence on the AI assistant, to fulfil certain needs in their lives, is ","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":5,"subdomain":"5.2"},{"ev_id":"24.09.03","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Cooperation","risk_subcategory":"Collective action problems","description":"\"Collective action problems are ubiquitous in our society (Olson Jr, 1965). They possess an incentive structure in which society is best served if everyone cooperates, but where an individual can achieve personal gain by choosing to defect while others cooperate. The way we resolve these problems at many scales is highly complex and dependent on a deep understanding of the intricate web of social interactions that forms our culture and imprints on our individual identities and behaviours (Ostrom, 2010). Some collective action problems can be resolved by codifying a law, for instance the social","entity":"Human","intent":"Other","timing":"Other","domain":5,"subdomain":"5.2"}]}