MIT AI Risk Repository · Risk Sub-Category · 62.16.16
Benchmark Limitations (Insufficient benchmarks for AI safety evaluation)
Category: Model Evaluations
Description
"Benchmarks dedicated to measuring the performance of AI systems (e.g., on programming or math tasks) are more well-developed than those for assessing safety and harms in AI systems [234]. This gap can lead to AI systems excelling in specific tasks while exhibiting harmful behaviors that go undetected. More safety-related evaluation datasets can help in identifying previously overlooked undesirable model behaviors."
From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 6.5 Governance failure
- Causal entity
- Other
- Intent
- Other
- Timing
- Pre-deployment
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
- Governance - Regulation