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Evidence record 7269 · automatically gathered

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups

Fairness in machine learning is predominantly evaluated through outcome-oriented metrics, such as Demographic parity, which measure whether predictions are statistically consistent across protected groups. However, these metrics cannot detect whether a model uses systematically different reasoning for different demographic groups, which violates procedural fairness principles. This problem is compounded by intersectionality, where models may appear fair on individual attributes (e.g., race) whil

Record details

Published: 13 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
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.

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How to cite this record

ethics.ai (13 March 2026), “MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups,” evidence record 7269, https://ethics.ai/record/7269 (originally published by arXiv).

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