Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness

Autor:	Jeanselme, Vincent, De-Arteaga, Maria, Zhang, Zhe, Barrett, Jessica, Tom, Brian
Rok vydání:	2022
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data. In healthcare, biases have marked medical history, leading to unequal care affecting marginalised groups. Patterns in missing data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is often an overlooked preprocessing step, with attention placed on the reduction of reconstruction error and overall performance, ignoring how imputation can affect groups differently. Our work studies how imputation choices affect reconstruction errors across groups and algorithmic fairness properties of downstream predictions. Comment: Full Journal Version under review; Presented at the conference Machine Learning for Health (ML4H) 2022 Published in the Proceedings of Machine Learning Research (193)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.06648 Zobrazit plný text záznamu View this record from Arxiv