Author

M. Vladimirova

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#machine learning Preprint Oct 2026

Debias Anything: Fairness with Diversity without Supervision in Diffusion Models

Although diffusion models produce high-quality images, they also reproduce and amplify demographic imbalances in their training data. Debiasing their generation process post-training w.r.t. some sensitive attribute usually relies on classifier guidance or explicit text extra-conditioning, but this reduces methods'appli...

Théau d'Audiffret, M. Vladimirova, Jean-Yves Franceschi · 0 citations

#artificial intelligence Preprint Aug 2026

Position: Fairness Failure in Generative Models is an Evaluation Problem

This position paper argues that fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem: fairness findings are rarely comparable across papers or actionable for deployment decisions.

M. Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth · 0 citations