As artificial intelligence systems increasingly influence decisions in finance, healthcare, hiring, education, and public services, questions of fairness have moved from academic debate to operational necessity. Models that perform well on average can still cause harm if their predictions vary unfairly across demographic groups. Ethical AI is not only about avoiding explicit bias but also about measuring and controlling subtle performance disparities that emerge across … Continue reading Ethical AI: Subpopulation Fairness Metrics and Preventing Unequal Model Performance