How Should AI Safety Benchmarks Benchmark Safety?

2026-01-30

AI Summary

This study addresses significant shortcomings in existing AI safety benchmarks, which inadequately assess advanced AI systems across technical, cognitive, and sociotechnical dimensions. Through a systematic review of 210 benchmarks, the work introduces classical risk management principles and measurement theory into the design of AI safety evaluations for the first time. It proposes a framework centered on measurability boundary analysis and probabilistic metric design. Combining systematic literature review, theoretical modeling, and hybrid evaluation methodologies, the research develops an actionable roadmap and a practical checklist for benchmark development. Empirical validation demonstrates the efficacy of the proposed approach, offering researchers and practitioners a rigorous, operational guide to constructing more robust and meaningful AI safety benchmarks.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

AI safety benchmarks are pivotal for safety in advanced AI systems; however, they have significant technical, epistemic, and sociotechnical shortcomings. We present a review of 210 safety benchmarks that maps out common challenges in safety benchmarking, documenting failures and limitations by drawing from engineering sciences and long-established theories of risk and safety. We argue that adhering to established risk management principles, mapping the space of what can(not) be measured, developing robust probabilistic metrics, and efficiently deploying measurement theory to connect benchmarking objectives with the world can significantly improve the validity and usefulness of AI safety benchmarks. The review provides a roadmap on how to improve AI safety benchmarking, and we illustrate the effectiveness of these recommendations through quantitative and qualitative evaluation. We also introduce a checklist that can help researchers and practitioners develop robust and epistemologically sound safety benchmarks. This study advances the science of benchmarking and helps practitioners deploy AI systems more responsibly.

Problem

• AI safety benchmarks
• risk management
• measurement theory
• sociotechnical shortcomings
• benchmark validity

Innovation

• AI safety benchmarking
• risk management
• measurement theory
• probabilistic metrics
• epistemic validity