2 papers
cs.HC2026
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…
cs.IR2024
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…