The Benchmark Trap: Structures of Power and Injustice in AI Evaluations
AI benchmarks are not neutral tools of evaluation but rather socio-technical artefacts that shape competition, power, and research priorities within AI. They standardize assessment and create leaderboards that reward state-of-the-art performance with prestige, citations, trust, and institutional influence. This concentration of rewards among powerful, industry-funded labs may perpetuate systematic harms affecting various actors in AI research.
AI benchmarks are not neutral tools of evaluation but rather socio-technical artefacts that shape competition, power, and research priorities within AI. They standardize assessment and create leaderboards that reward state-of-the-art performance with prestige, citations, trust, and institutional influence. This concentration of rewards among powerful, industry-funded labs may perpetuate systematic harms affecting various actors in AI research.
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Why it matters: This matters to AI researchers because current benchmarking practices may limit the field's ability to advance in epistemically robust and socially beneficial ways by reinforcing existing power structures and narrowing possible research trajectories.
Source: https://arxiv.org/abs/2608.15326
This article was originally published at: https://arxiv.org/abs/2608.15326