An empirical characterization of fair machine learning for clinical risk prediction.

ABSTRACT: The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable attention and criticism. However, the appropriateness of this framework is unclear due to both ethical as well as technical considerations, the latter of which include trade-offs between measures of fairness and model performance that are not well-understood for predictive models of clinical outcomes. To inform the ongoing debate, we conduct an empirical study to characterize the impact of penalizing group fairness violations on an array of measures of model performance and group fairness. We repeat the analysis across multiple observational healthcare databa

SUBMITTER: Pfohl SR 

PROVIDER: S-EPMC7871979 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature