arXiv AI July 17, 2026 By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv:2607. 14984v1 Announce Type: new Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 27

FRAME: separating sampling variation from representational cause in medical imaging fairness

The paper introduces FRAME, a two‑step framework for auditing fairness claims in medical imaging. First, it derives a fair‑model reference distribution that captures the portion of subgroup performance differences attributable to sampling variation. Second, it tests the remaining difference using operators in representation space to assess whether demographic information or disease entanglement drives the bias. Across a large dataset of 702,206 images and 36 encoders, the reference explains a substantial median share of race and age differences, while interventions such as injecting demographic decodability or entangling disease direction have limited impact on the residual bias.

By Mahshad Lotfinia, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh

multimodal safety

arXiv AI

Jul 24

Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test

arXiv:2607. 20787v1 Announce Type: cross Abstract: For two decades, the standard remedy for class-imbalanced learning has been to fabricate synthetic minority examples, and the standard evidence of their validity has been a check that cannot fail: synthetic points are scored against the very data that generated them.

By Ahmad B. Hassanat, Ahmad S. Tarawneh, Ghada A. Altarawneh

safety

arXiv Machine Learning

Jul 15

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

arXiv:2607. 12464v1 Announce Type: cross Abstract: When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task.

By Jeeyung Kim, Erfan Esmaeili, Qiang Qiu

diffusion computer-vision reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 15

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...

By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas

safety

arXiv Machine Learning

Jun 19

EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning

arXiv:2510. 19893v2 Announce Type: replace Abstract: Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations.

By Shiqi Dai, Wei Dai, Jiaee Cheong, Paul Pu Liang

reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 9

CompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation

arXiv:2603. 16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that generators produce equally high-quality images across demographic groups.

By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier

rag diffusion fine-tuning safety

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Related stories

FRAME: separating sampling variation from representational cause in medical imaging fairness

Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning

CompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation