2022/06/04 by Vinith M. Suriyakumar, Marzyeh Ghassemi, Suriyakumar, Vinith M. +3 · 1 citation
Computer Science · Mathematics · Medicine · #Advanced Causal Inference Techniques #Artificial Intelligence in Healthcare and Education #Computers and Society (cs.CY) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2206.02058
openalex publication_date 2022/06/04 · openalex created_date 2022/06/13 · openalex updated_date 2026/07/28
Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are personalized with group attributes can reduce performance at a group level. We propose formal conditions to ensure the "fair use" of group attributes in prediction tasks by training one additional model -- i.e., collective preference guarantees to ensure that each group who provides personal data will receive a tailored gain in performance in return. We present sufficient conditions to ensure fair use in empirical risk minimization and characterize failure modes that lead to fair use violations due to standard practices in model development and deployment. We present a comprehensive empirical study of fair use in clinical prediction tasks. Our results demonstrate the prevalence of fair use violations in practice and illustrate simple interventions to mitigate their harm.