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Fairness in AI and machine learning systems has become a fundamental problemin the accountability of AI systems. While the need for accountability of AImodels is near ubiquitous, healthcare in particular is a challenging fieldwhere accountability of such systems takes upon additional importance, asdecisions in healthcare can have life altering consequences. In this paper wepresent preliminary results on fairness in the context of classification parityin healthcare. We also present some exploratory methods to improve fairness andchoosing appropriate classification algorithms in the context of healthcare.

Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare