Browse AI risks

60 entries · page 2 of 2

1. 65.15.03 · Risk Sub-Category

   Output risks (Value alignment)

   Over- or under-reliance

   "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."

   From AI Risk Atlas (IBM2025)

   Domain 5 5.1 Human Unintentional Post-deployment

2. 66.07.05 · Risk Sub-Category

   Psychological

   Over-reliance

   "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"

   From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

   Domain 5 5.1 Human Other Post-deployment

3. 66.11.03 · Risk Sub-Category

   Physical

   Self-harm

   "A person who deliberately damages their own body as a direct or indirect result of using a technology system"

   From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

   Domain 5 5.1 Human Intentional Post-deployment

4. 69.08.00 · Risk Category

   Attempts to fulfill inappropriate role

   "The chatbot poses as a human or attempts to fill a role in a way that fails to match human expectations."

   From Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024)

   Domain 5 5.1 AI Unintentional Other

5. 69.09.00 · Risk Category

   Forms emotional bonds

   "The chatbot elicits emotional or social dependence."

   From Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024)

   Domain 5 5.1 AI Other Other

6. 69.09.02 · Risk Sub-Category

   Forms emotional bonds

   Then violates those bonds

   —

   From Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024)

   Domain 5 5.1 AI Other Other

7. 69.09.04 · Risk Sub-Category

   Forms emotional bonds

   Over-reliance/addiction

   —

   From Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024)

   Domain 5 5.1 Other Other Other

8. 70.04.04 · Risk Sub-Category

   Social Risks

   Unhealthy or dangerous human-EAI relationships

   "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]."

   From Embodied AI: Emerging Risks and Opportunities for Policy Action (Perlo2025)

   Domain 5 5.1 Other Unintentional Post-deployment

9. 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

   From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

   Domain 5 5.1 Human Unintentional Post-deployment

10. 73.04.03 · Risk Sub-Category

    LLM-Systems Can Be Untrustworthy

    Overreliance

    "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

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

    Domain 5 5.1 Human Unintentional Post-deployment

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.