AI in healthcare raises urgent questions about bias, privacy, and power. Safiya U. Noble, Ph.D., examines how AI systems can reproduce social and racial inequities when they rely on incomplete data, hidden assumptions, and proxies such as healthcare spending. Noble points to problems in search engines, image generation, facial recognition, and medical algorithms, including cases where systems mislabel darker skin, fail more often on Black women, or favor white patients over sicker Black patients. She also highlights the risks of turning sensitive public and patient data over to large technology companies. Rather than treating AI as a neutral solution, Noble emphasizes the need for human judgment, community participation, stronger data protections, and smaller expert models with local control so healthcare decisions better reflect people’s real lives and social context. Series: "Exploring Ethics" [Health and Medicine] [Humanities] [Science] [Show ID: 41364]