9/9/21 | 4:15pm | E51-149

Hamsa Bastani

Assistant Professor
Wharton

Abstract: A key aspect of human intelligence is their ability to convey their knowledge to others in succinct forms. However, despite their predictive power, current machine learning models are largely blackboxes, making it difficult for humans to extract useful insights. Focusing on sequential decision-making, we design a novel machine learning algorithm that conveys its insights to humans in the form of interpretable "tips". Our algorithm selects the tip that best bridges the gap in performance between human users and the optimal policy. We evaluate our approach through a series of randomized controlled user studies where participants manage a virtual kitchen. Our experiments show that the tips generated by our algorithm can significantly improve human performance relative to intuitive baselines. In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI collaboration. For instance, we find evidence that participants do not simply blindly follow our tips; instead, they combine them with their own experience to discover additional strategies for improving performance. Joint work with Osbert Bastani (Penn) and Park Sinchaisri (Berkeley).The link to the full paper can be found at: https://arxiv.org/abs/2108.08454.

Bio: Hamsa Bastani is an Assistant Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, social good, and revenue management. She designs methods for sequential decision-making, transfer learning and human-in-the-loop analytics. Her applied work uses large-scale, novel data sources to inform policy around impactful societal problems. Her work has received several recognitions, including the Pierskalla Award for the best paper in healthcare, as well as first place in the George Nicholson and MSOM student paper competitions.