  Healthcare programs such as Medicaid provide crucial services to vulnerable
populations, but due to limited resources, many of the individuals who need
these services the most languish on waiting lists. Survival models, e.g. the
Cox proportional hazards model, can potentially improve this situation by
predicting individuals' levels of need, which can then be used to prioritize
the waiting lists. Providing care to those in need can prevent
institutionalization for those individuals, which both improves quality of life
and reduces overall costs. While the benefits of such an approach are clear,
care must be taken to ensure that the prioritization process is fair or
independent of demographic information-based harmful stereotypes. In this work,
we develop multiple fairness definitions for survival models and corresponding
fair Cox proportional hazards models to ensure equitable allocation of
healthcare resources. We demonstrate the utility of our methods in terms of
fairness and predictive accuracy on two publicly available survival datasets.
