Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptak
Record details
Published: 7 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (7 April 2026), “Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program,” evidence record 6229, https://ethics.ai/record/6229 (originally published by arXiv).
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