In generative recommender systems, adopting diffusion-based learning primarily for accuracy often reproduces the biased interaction distributions present in historical logs, which results in systematic disparities for both users and items. Fairness-aware auditing can enable responsible diffusion recommendation by revealing when utility gains are obtained through consumer- or provider-side inequities, as instantiated in this study. We …