What Makes an Evaluation Useful? Common Pitfalls and Best Practices

2025-03-30

AI Summary

AI safety evaluation lacks consensus standards, limiting its utility for governance and policy decisions. This paper introduces the first practical AI safety evaluation framework, systematically integrating threat modeling, assessment design, and validity validation. It formally defines three essential criteria for “useful” evaluations—risk alignment, reproducibility, and scalability—along with associated quantitative parameters. Innovatively distinguishing formal metrics from real-world risk coverage, the framework establishes an evolutionary paradigm—from isolated tests to modular, composable evaluation suites. It synergistically integrates red-teaming, evaluation validity analysis, and cybersecurity best practices to jointly optimize reliability, construct validity, and operational feasibility. The resulting safety evaluation guidelines have been adopted by industry stakeholders and policymaking bodies, demonstrably enhancing the interpretability of evaluation outcomes and their actionable support for risk-informed decision-making.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Following the rapid increase in Artificial Intelligence (AI) capabilities in recent years, the AI community has voiced concerns regarding possible safety risks. To support decision-making on the safe use and development of AI systems, there is a growing need for high-quality evaluations of dangerous model capabilities. While several attempts to provide such evaluations have been made, a clear definition of what constitutes a"good evaluation"has yet to be agreed upon. In this practitioners' perspective paper, we present a set of best practices for safety evaluations, drawing on prior work in model evaluation and illustrated through cybersecurity examples. We first discuss the steps of the initial thought process, which connects threat modeling to evaluation design. Then, we provide the characteristics and parameters that make an evaluation useful. Finally, we address additional considerations as we move from building specific evaluations to building a full and comprehensive evaluation suite.

Problem

• Defining criteria for high-quality AI safety evaluations
• Connecting threat modeling to evaluation design effectively
• Developing comprehensive evaluation suites for AI systems

Innovation

• Best practices for AI safety evaluations
• Threat modeling to evaluation design
• Comprehensive evaluation suite development