We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability). This approach uses a training loss function that does not know any demographic information to treat as sensitive. Despite this, we demonstrate that our proposed approach often scores better on recently established fairness metrics (without a significant drop in prediction accuracy) compared to various baselines, including ones which directly use sensitive demographic information in their training loss. Our code is available at: https://github.com/discovershu/DRO_COX
翻译:我们提出了一个培训生存分析模型的一般方法,最大限度地减少所有人口群中规模足够大(至少具有用户指定的最低概率)的最坏情况错误。这个方法使用的培训损失功能并不了解任何人口信息可被视为敏感。尽管如此,我们还是表明,与各种基线相比,我们所提议的方法往往比最近确立的公平度量(预测准确度没有显著下降)得分更高,包括直接使用敏感人口信息进行培训损失的基线。我们的代码可以在https://github.com/discovershu/DRO_COX上查到。