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Evidence record 3048 · automatically gathered

FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness

Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle. This work presents FairSelect, a toolkit for systematically evaluating fairness mitigation strategies applied individually and in

Record details

Published: 9 July 2026
Source: arXiv fairness query
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.

Shared topic: Bias & fairness

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How to cite this record

ethics.ai (9 July 2026), “FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness,” evidence record 3048, https://ethics.ai/record/3048 (originally published by arXiv fairness query).

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