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Evidence record 13812 · automatically gathered

Fairness Interventions in Classification: A Study on AI Explainability

arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds. Our main argument is that even as a gap in Demographic Parity is used to diagnose inequality between groups, Equalized Odds constitutes a more reliable fairness crit

Record details

Published: 28 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 28 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: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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How to cite this record

ethics.ai (28 July 2026), “Fairness Interventions in Classification: A Study on AI Explainability,” evidence record 13812, https://ethics.ai/record/13812 (originally published by arXiv cs.CY).

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