MIT AI Risk Repository · Risk Sub-Category · 61.02.12
Challenges in perceiving, measuring, and recognizing harm
Category: Sources of systemic risks from general-purpose AI
Description
"Harm from AI often manifests subtly or over the long term, making it difficult to identify, measure, and address effectively."
From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 6.5 Governance failure
- Causal entity
- Other
- Intent
- Other
- Timing
- Other
Subdomain definition: Inadequate regulatory frameworks and oversight mechanisms failing to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.
Real-world incidents in this subdomain
- Nippon Life Alleged ChatGPT Practiced Law Without a License in Illinois Disability Case
- OpenAI Allegedly Did Not Alert RCMP After ChatGPT Flagged Violent Chats Before British Columbia School Shooting
- Remotely Operated Taser-Armed Drones Proposed by Taser Manufacturer as Defense for School Shootings in the US
How other frameworks describe this risk
- Mobility
- Liability issues in case of accidents
- Faster scientific progress makes it harder for governance to keep pace with development
- Type 1: Diffusion of responsibility
- Products Liability Law
- Institutional responsibilities
- Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
- Benchmarking (Cross-lingual data contamination)