[2603.24396] Exploring How Fair Model Representations Relate to Fair Recommendations

Computer Science > Information Retrieval arXiv:2603.24396 (cs) [Submitted on 25 Mar 2026] Title:Exploring How Fair Model Representations Relate to Fair Recommendations Authors:Bjørnar Vassøy, Benjamin Kille, Helge Langseth View a PDF of the paper titled Exploring How Fair Model Representations Relate to Fair Recommendations, by Bj{\o}rnar Vass{\o}y and 2 other authors View PDF HTML (experimental) Abstract:One of the many fairness definitions pursued in recent recommender system research targets mitigating demographic information encoded in model representations. Models optimized for this definition are typically evaluated on how well demographic attributes can be classified given model representations, with the (implicit) assumption that this measure accurately reflects \textit{recommendation parity}, i.e., how similar recommendations given to different users are. We challenge this assumption by comparing the amount of demographic information encoded in representations with various measures of how the recommendations differ. We propose two new approaches for measuring how well demographic information can be classified given ranked recommendations. Our results from extensive testing of multiple models on one real and multiple synthetically generated datasets indicate that optimizing for fair representations positively affects recommendation parity, but also that evaluation at the representation level is not a good proxy for measuring this effect when comparing models. We al...