Longitudinal Fairness with Censorship

TL;DR

This paper addresses fairness in longitudinal, censored data settings where event times are unknown, proposing new measures and algorithms to mitigate bias in sensitive applications like medicine and justice.

Contribution

It introduces fairness measures and a debiasing algorithm tailored for censored data environments, bridging the gap between fairness in censored and uncensored contexts.

Findings

Effective fairness measures for censored data

Debiasing algorithm improves fairness in experiments

Validated on four real-world censored datasets

Abstract

Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, propose a debiasing algorithm, and provide necessary theoretical constructs to bridge fairness with and without censorship for these important and socially-sensitive tasks. Our experiments on four censored datasets confirm the utility of our approach.