1 citations · 1 across the 7 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
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.IR2026
Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem
Anas Buhayh, Elizabeth McKinnie, Clement Canel +1
Optimizing outcomes for multiple stakeholders in recommender systems has historically focused on algorithmic interventions, such as developing multi-objective models or re-ranking…