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Explore the complex relationship between fairness and accuracy in machine learning applications for healthcare through this conference talk by Omer Reingold from Stanford University. Examine how machine-learning models used in clinical decision-making, resource allocation, and patient risk assessment raise important concerns about fairness, particularly when performance varies across demographic or clinical subgroups. Challenge the common assumption that fairness and accuracy are competing objectives by investigating contemporary research in algorithmic fairness that demonstrates this tradeoff doesn't always hold. Discover how fairness notions such as multicalibration and indistinguishability-based definitions can actually enhance the reliability and robustness of predictive models. Learn how multicalibration bridges actuarial group-level and clinical individual-level risk analysis by ensuring predictions remain statistically valid across diverse, potentially overlapping subpopulations. Understand how these fairness guarantees create models that are robust to distribution shifts and adaptable to evolving downstream objectives and constraints without requiring retraining. Analyze specific scenarios where fairness requirements do create genuine tensions with predictive accuracy and explore why such tradeoffs may be unavoidable, presenting policy decisions that healthcare systems must address.
