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stat.ML2025
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal +12
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal pe…
stat.ML2017★ 56 cited
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
David Madras, Toniann Pitassi, Richard Zemel
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorit…