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Improving Human Decision-Making with Machine Learning
Hamsa Bastani · Osbert Bastani · Park Sinchaisri

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 interfaces. 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.

Author Information

Hamsa Bastani (Wharton School, University of Pennsylvania)

My research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, revenue management, and social good. Recently, I've been working on the design and application of transfer learning algorithms, e.g., for predictive analytics with small data, dynamic pricing across related products, and speeding up clinical trials with surrogate outcomes. I am also interested in algorithmic accountability and using big data to combat social and environmental harm.

Osbert Bastani (University of Pennsylvania)
Park Sinchaisri (University of California, Berkeley)

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