Authors: Tianyang Xie, Yong Ge
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).
Source: http://arxiv.org/abs/2502.02567v1
