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

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 hinge s on specific time frames . To address this critical research gap, we introduce a new fairness concept: equalized odds (EO) in survival analysis, which emphasize s prediction fairness at pre - defined time points . To achieve th e 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 a n innovative censored data augmentation technique. Our CMIA approach can effectively balance prediction accuracy and fairness, and it is applicable to various survival models. W e 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 disparit y while maintaining good accuracy and significantly outperform s the other competing methods across multiple datasets and survival models (e.g., linear COX, deep AFT) . Keywords: survival analysis, equalized odds, fairness, pre - defined evaluation time points, conditional mutual information, cen sore d data augmentation 2 Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach 1. INTRODUCTION Survival analysis is a set of statistical methods designed to model data where the outcome of interest is the time to the occurrence of a particular event (P . It is widely applied across many domains, such as healthcare (Khuri et al., 2005; Reddy et al., 2015), education (Ameri et al., 2016), business intelligence (Li et al., 2016; Rakesh et al., 2016), etc . In these applications, survival analysis provide s likelihood estimation for the occurrence of event s over time, which is useful for a lot of crucial decision making.