1 citations · 1 across the 4 of their papers we have counts for
4 papers
Co-Designing Organizational Justice Indicators for Algorithmic Systems
Fujiko Robledo Yamamoto, Nicholas Mattei, Pradeep Ragothaman +2
Fairness in machine learning is often conceptualized narrowly in comparative, distributional terms. In studying stakeholders' concepts of fairness, we find that this framing is ins…
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…
Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
Amanda Aird, Cassidy All, Paresha Farastu +4
Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations. A social choice formulation of the…