Abstract:

Artificial intelligence holds enormous promise to automate our life. At the same time, AI systems can be incredibly brittle, unreliable, and biased. In this talk, I will present a few approaches towards making AI more reliable, trustworthy, and fair, drawing on recent work on uncertainty quantification and algorithmic fairness. Specifically, I will discuss how to make large language models more calibrated, how to construct prediction sets for data with multiple missing outcomes, and how to obtain optimally accurate fair classifiers under constraints on linear disparity measures.

Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/97670810812