Berkeley Computational Social Science Forum — Improving Human Decision-Making with Machine Learning

CSS Training Program

February 15, 2022
4:00pm to 5:00pm
Virtual Participation

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Berkeley Computational Social Science Forum
Date: Tuesday, February 15, 2022
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom

Improving Human Decision-Making with Machine Learning

Park Sinchaisri, Assistant Professor of Operations & IT Management, UC Berkeley Haas School of Business
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 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. Paper: https://arxiv.org/abs/2108.08454

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information or if you are interested in presenting current research for an upcoming session.

Speaker(s)

Park Sinchaisri

Assistant Professor of Operations & IT Management, UC Berkeley Haas School of Business

Park Sinchaisri is an Assistant Professor of Operations & IT Management at UC Berkeley Haas School of Business. His research combines tools from operations, economics, machine learning, and behavioral sciences to study how to manage the future of work and the human-AI interfaces. He received a PhD in Operations, Information & Decisions and an MA in Statistics from Wharton, an SM in Computational Science & Engineering from MIT, and an ScB in Computer Engineering & Applied Mathematics-Economics from Brown. Growing up in Bangkok, Park hopes to expand his research to make a positive social impact, from solving urban problems to helping the marginalized work populations.