46 citations · 119 across the 11 of their papers we have counts for
3 papers · 1 filter
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
Yuyan Wang, Xuezhi Wang, Alex Beutel +3
As multi-task models gain popularity in a wider range of machine learning applications, it is becoming increasingly important for practitioners to understand the fairness implicati…
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
Flavien Prost, Pranjal Awasthi, Nick Blumm +7
In this work we study the problem of measuring the fairness of a machine learning model under noisy information. Focusing on group fairness metrics, we investigate the particular b…
Measuring Recommender System Effects with Simulated Users
Sirui Yao, Yoni Halpern, Nithum Thain +6
Imagine a food recommender system -- how would we check if it is \emph{causing} and fostering unhealthy eating habits or merely reflecting users' interests? How much of a user's ex…