4 papers
Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation
Amanda Aird, Ben Armstrong, Nicholas Mattei +1
Envy-freeness and the relaxation to Envy-freeness up to one item (EF-1) have been used as fairness concepts in the economics, game theory, and social choice literatures since the 1…
Fairness for niche users and providers: algorithmic choice and profile portability
Elizabeth McKinnie, Anas Buhayh, Clement Canel +1
Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness prop…
Social Choice for Heterogeneous Fairness in Recommendation
Amanda Aird, Elena Štefancová, Cassidy All +4
Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has…
Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking
Elena Stefancova, Cassidy All, Joshua Paup +3
Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world s…