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Explore the critical importance of context and specificity in AI research through this colloquium presentation by Google Research Scientist Katherine Heller, who leads the Context in AI Research (CAIR) team. Discover methodologies for identifying and mitigating robustness and fairness issues in medical AI applications, with emphasis on human-centered approaches that ensure inclusive technology development. Learn about advanced evaluation techniques, out-of-distribution learning strategies, and causal methods designed to reduce harm to underrepresented groups in healthcare AI systems. Examine real-world implementations including sepsis detection systems integrated into Duke University Hospital Emergency Departments, surgical complication prediction tools, and nationally deployed mobile studies for Multiple Sclerosis research. Understand how globally sensitive data collection practices improve contextual understanding in generative AI systems, and explore innovative methods for analyzing time series wearable data to predict mood disorders in maternal health. Gain insights into the intersection of machine learning and biomedical research, focusing on how biomedical challenges drive foundational advances in AI while ensuring equitable outcomes across diverse populations.
