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Inherent Trade-Offs in Algorithmic Fairness

Recent discussion in the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs...
Location: Corwin Hall, Room 127
Tags: Seminars

Physics-informed Machine Learning For Climate, Urban, And Environmental Sciences

At the confluence of scientific simulation and modern machine learning there exists an opportunity to develop a “middle path” that leverages the strengths of both approaches to build machine-learning emulators of numerical simulation models. From the perspective of machine learning, incorporating simulation data may significantly reduce the need...
Location: Bowen Hall Auditorium Room 222
Tags: Seminars