2 papers
cs.IR2022
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen +9
There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure e…
cs.LG2021
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…