论文标题
分配强大的生存分析：没有人口统计的新型公平损失
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
论文作者
论文摘要
我们提出了一种训练生存分析模型的一般方法，该方法可以最大程度地减少所有足够大的亚种群中最严重的错误（至少发生用户指定的最小概率）。这种方法使用训练损失函数，该函数不知道任何人口统计信息以将其视为敏感。尽管如此，我们证明了与各种基线相比，我们提出的方法通常在最近确定的公平度量标准（没有明显下降的预测准确性下降）上得分更好，其中包括直接在培训损失中直接使用敏感人群信息的基线。我们的代码可在以下网址找到：https：//github.com/discovershu/dro_cox
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
