A Concise Framework for Fairness: Navigating Disparate Impact in Healthcare AI (Preprint)

UNSTRUCTURED As artificial intelligence (AI) increasingly permeates healthcare, it promises to enhance patient outcomes and operational efficiency. However, the integration of AI also introduces significant risks of perpetuating biases, necessitating careful consideration of fairness in these systems. This paper proposes a comprehensive framework aimed at mitigating biases and promoting fairness within healthcare AI. By outlining a structured approach encompassing all stages of the AI lifecycle—from data collection and preprocessing to model selection and continuous monitoring—we provide actionable guidance for developers, researchers, and healthcare professionals. Furthermore, we introduce specific fairness metrics such as False Positive/Group Size Parity and False Discovery Rate Parity, which are crucial for evaluating AI systems in various healthcare applications—from diagnostic tools to resource allocation.