  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
