Benchmark Limitations (Insufficient benchmarks for AI safety evaluation)
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.
ENTITY
3 - Other
INTENT
3 - Other
TIMING
1 - Pre-deployment
Risk ID
mit1124
Domain lineage
6. Socioeconomic and Environmental
6.5 > Governance failure
Mitigation strategy
1. Establish consensus-driven, statistically rigorous safety-oriented evaluation datasets (e.g., concerning bias, toxicity, misuse, and security) to ensure construct validity and representativeness of real-world risk, moving beyond performance-based metrics. 2. Mandate the integration of dynamic evaluation methodologies, such as AI Red Teaming and adversarial prompting, throughout the full AI lifecycle (including pre-release and runtime monitoring) to proactively detect emergent or 'out-of-distribution' harmful model behaviors that static benchmarks often fail to capture. 3. Formally adopt and adhere to a recognized AI Risk Management Framework (e.g., NIST AI RMF or ISO/IEC 42001) to structure and govern the continuous process of identifying, measuring, and managing risks, thereby systematically closing the gap between model capability and safety evaluation rigor.