Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically-conditioned synthetic generator can do both: mitigate bias on the training side and detect bias on the evaluation side. Working on COVID-19 chest CT classification with an end-to-end fine-tuned Stabl
Record details
Published: 16 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 18 July 2026
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ethics.ai (16 July 2026), “Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers,” evidence record 11353, https://ethics.ai/record/11353 (originally published by arXiv).
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