Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
This work addresses the critical problem of ensuring fair AI systems in healthcare, where model decisions can have life-altering consequences for patients.
This paper explores fairness in machine learning models within healthcare, specifically focusing on classification parity. It presents preliminary results and discusses exploratory methods for improving fairness and selecting appropriate classification algorithms in this domain.
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.