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).

翻译：生存分析作为预测事件发生时间的重要工具，已在医疗保健、刑事司法和金融等多个领域得到广泛应用。与分类任务类似，生存分析可能对弱势群体表现出偏见，这通常源于数据或算法中固有的偏差。信息系统和计算机科学领域的多项研究已尝试解决生存分析中的公平性问题。然而，现有方法往往忽视了在预定义评估时间点预测公平性的重要性，而在现实应用中，决策制定往往依赖于特定时间框架。为填补这一关键研究空白，我们提出了生存分析中的新公平性概念：均衡机会，该概念强调在预定义时间点的预测公平性。为实现生存分析中的均衡机会公平性，我们提出了一种条件互信息增强方法，其特点在于基于条件互信息的新型公平性正则化项以及创新的删失数据增强技术。我们的条件互信息增强方法能有效平衡预测准确性与公平性，并适用于多种生存模型。我们在三个不同应用领域中，将条件互信息增强方法与多种先进方法进行比较评估，结果表明：该方法在保持良好准确性的同时持续降低预测差异，并在多个数据集和生存模型（如线性COX模型、深度AFT模型）上显著优于其他竞争方法。