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Evidence record 184 · automatically gathered

Whose fairness? Structural concentration in AI bias research

Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Yet the fairness definitions, benchmarks, and debiasing frameworks on which this methodology rests are treated as universal while being produced by a research community whose composition has never been characterized. We show that the AI bias research are structurally concentrated, and that this

Record details

Published: 6 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

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How to cite this record

ethics.ai (6 July 2026), “Whose fairness? Structural concentration in AI bias research,” evidence record 184, https://ethics.ai/record/184 (originally published by arXiv).

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