Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach

TL;DR

This paper introduces a new fairness concept called equalized odds for survival analysis and proposes a conditional mutual information augmentation method to improve fairness at specific evaluation time points while maintaining accuracy.

Contribution

The paper presents a novel fairness regularization technique based on conditional mutual information and censored data augmentation for survival analysis.

Findings

CMIA reduces prediction disparity across groups.

It maintains high prediction accuracy.

Outperforms existing fairness methods in multiple datasets.

Abstract

Survival analysis, a vital tool for predicting the time to event, has been used in many domains such as healthcare, criminal justice, and finance. Like classification tasks, survival analysis can exhibit bias against disadvantaged groups, often due to biases inherent in data or algorithms. Several studies in both the IS and CS communities have attempted to address fairness in survival analysis. However, existing methods often overlook the importance of prediction fairness at pre-defined evaluation time points, which is crucial in real-world applications where decision making often hinges on specific time frames. To address this critical research gap, we introduce a new fairness concept: equalized odds (EO) in survival analysis, which emphasizes prediction fairness at pre-defined time points. To achieve the EO fairness in survival analysis, we propose a Conditional Mutual Information Augmentation (CMIA) approach, which features a novel fairness regularization term based on conditional mutual information and an innovative censored data augmentation technique. Our CMIA approach can effectively balance prediction accuracy and fairness, and it is applicable to various survival models. We evaluate the CMIA approach against several state-of-the-art methods within three different application domains, and the results demonstrate that CMIA consistently reduces prediction disparity while maintaining good accuracy and significantly outperforms the other competing methods across multiple datasets and survival models (e.g., linear COX, deep AFT).