Ethical AI in Healthcare: A Data Science Perspective on Bias, Fairness, and Transparency

Abstract

As artificial intelligence (AI) becomes increasingly integrated into healthcare, concerns about bias, fairness, and transparency have come to the forefront. This paper examines how data science can address these ethical challenges by developing robust frameworks for AI model evaluation and deployment. By combining insights from data science, bioethics, and clinical practice, we explore strategies for mitigating bias in healthcare algorithms and ensuring equitable outcomes. Case studies from Australian healthcare systems illustrate the importance of interdisciplinary collaboration in building trustworthy AI systems.