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…
Understanding Data Usage when Making High-Stakes Frontline Decisions in Homelessness Services
Teale W. Masrani, Geoffrey Messier, Amy Voida +2
Frontline staff of emergency shelters face challenges such as vicarious trauma, compassion fatigue, and burnout. The technology they use is often not designed for their unique need…
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…
Dynamic fairness-aware recommendation through multi-agent social choice
Amanda Aird, Paresha Farastu, Joshua Sun +5
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers st…