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.
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
Related publications
Previous
Gated Against One Model, Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks
Next
Pluralistic Human-Robot Interaction: Designing for Robot Interaction with Diverse Communities
Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior
Executive Guide
Pluralistic Human-Robot Interaction: Designing for Robot Interaction with Diverse Communities
Executive Guide
Gated Against One Model, Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks
Executive Guide
Benchmarking Identity-Sensitive LLM Outputs for Surveillance and Security Robots
Executive Guide
Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark
Executive Guide
When Is an Agent Evaluation Over? Outcome Finality and Cross-Unit Separation
Executive Guide
Download & citation
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
Verification
This is an authenticated institutional record.
- 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.