1 min readExecutive Guide

Executive Guide

The Benchmark Trap: Structures of Power and Injustice in AI Evaluations

Author
Aziz Shuaib Ausi
Published
August 19, 2026
Reading time
1 min
Publication type
Executive Guide
Availability
Open access

Executive Summary

An analysis of AI benchmarks reveals they are not neutral evaluative tools but socio-technical artifacts that influence competition, power dynamics, and research priorities within artificial intelligence. These benchmarks, by standardizing assessment and fostering leaderboards, concentrate prestige, citations, trust, and institutional influence towards entities capable of achieving state-of-the-art performance. The rising costs associated with developing competitive AI systems lead to a disproportionate concentration of these rewards among powerful, industry-funded research laboratories. This framework positions current benchmarking practices as potentially perpetuating systematic harms and structural injustices within the AI research ecosystem.

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An analysis of AI benchmarks reveals they are not neutral evaluative tools but socio-technical artifacts that influence competition, power dynamics, and research priorities within artificial intelligence. These benchmarks, by standardizing assessment and fostering leaderboards, concentrate prestige, citations, trust, and institutional influence towards entities capable of achieving state-of-the-art performance. The rising costs associated with developing competitive AI systems lead to a disproportionate concentration of these rewards among powerful, industry-funded research laboratories. This framework positions current benchmarking practices as potentially perpetuating systematic harms and structural injustices within the AI research ecosystem.

Why it matters

The critical assessment of AI benchmarking practices highlights a foundational issue in the development and governance of artificial intelligence. Understanding how these tools shape research direction, resource allocation, and power dynamics is crucial for organizations investing in or relying on AI, as it directly impacts innovation, ethical considerations, and the competitive landscape. This perspective suggests the need for strategic consideration of how AI evaluation systems are designed and implemented to foster a more equitable and effective research environment.

Key insights

  • AI benchmarks function as socio-technical artifacts, not merely neutral evaluation tools.
  • They actively shape competition, power structures, and research priorities in AI.
  • Benchmarks standardize system assessment and generate leaderboards that reward top performance with prestige, citations, trust, and influence.
  • The increasing cost of developing competitive AI systems leads to a concentration of rewards within powerful, often industry-funded, laboratories.
  • Current benchmarking practices may perpetuate systematic harms and structural injustices for various actors in AI research, aligning with theories of oppression.
  • The source explicitly states these concerns are situated within Iris Marion Young's theories of oppression and structural injustice.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.15326

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Cite this publication (APA 7)

Aziz Shuaib Ausi (2026). The Benchmark Trap: Structures of Power and Injustice in AI Evaluations. Executive Guide. Aziz Shuaib Ausi. https://www.azizshuaib.com/verify/ASA-EXG-2026-00405

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Verification ID
ASA-EXG-2026-00405
Version
v1.0 · r0
Issued
8/19/2026
Publisher
Aziz Shuaib Ausi
Licence
All rights reserved. Reproduction requires written permission.

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