Reliability and Validity in Artificial Intelligence

Catalog description

This course examines the principles and methods required to make artificial intelligence (AI) systems reliable and scientifically sound. Topics include evaluation and benchmarking, notions of validity, distribution shift, causality, predictive inference, AI-assisted statistical inference, data attribution, and beyond. Problem sets will involve both mathematical components and coding projects to see the practical effects of the methods we develop.