Position: Fairness Failure in Generative Models is an Evaluation Problem

The paper argues that fairness failures in generative models arise mainly from inadequate evaluation practices, making fairness findings hard to compare or use for deployment. It diagnoses common empirical and conceptual shortcomings in current methods and calls for a move toward standardized, generative‑specific evaluation. The authors introduce Fairness Cards, a minimal reporting artifact that explicitly documents evaluation choices—such as prompt families, counterfactual protocols, metrics, and refusal handling—to improve reproducibility, comparability, and accountability.

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.

• safety