Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation
arXiv:2605.30916v1 Announce Type: new Abstract: AI benchmarks have well-documented limitations, with prior work examining contamination, saturation, and construct underspecification. Aggregation has received far less attention: benchmarks are typically summarized by uniformly averaging item-level scores, implicitly treating every test item as equally valuable. We model benchmarking as a multitask principal-agent game and show that the welfare loss from a benchmark is determined jointly by three item-level primitives: alignment with normative welfare priorities, marginal improvability, and perf
The proliferation of AI systems and their increasing impact necessitate more robust and reliable benchmarking methodologies.
Improving AI benchmarking directly impacts the development, deployment, and public perception of AI, influencing investment and regulatory decisions.
This research provides a theoretical framework for designing more effective and fair AI benchmarks, moving beyond simplistic aggregation methods.
- · AI researchers
- · AI developers
- · AI ethics and safety organizations
- · Developers relying on flawed benchmarks
- · AI systems performing well on unrepresentative benchmarks
AI benchmark design will become more sophisticated, moving away from uniform aggregation.
Improved benchmarks will lead to better-aligned and more robust AI system development.
More reliable evaluation could accelerate AI adoption in critical sectors by increasing trust and understanding of AI capabilities.
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Read at arXiv cs.LG