  Fairness in AI and machine learning systems has become a fundamental problem
in the accountability of AI systems. While the need for accountability of AI
models is near ubiquitous, healthcare in particular is a challenging field
where accountability of such systems takes upon additional importance, as
decisions in healthcare can have life altering consequences. In this paper we
present preliminary results on fairness in the context of classification parity
in healthcare. We also present some exploratory methods to improve fairness and
choosing appropriate classification algorithms in the context of healthcare.
