"Auditing for Human Expertise."

Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, and Dennis Shung. In NeurIPS 2023, 2023. Related Blog Post.

"Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits."

Raghavan, Manish, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. SIAM Journal on Computing Vol. 52, No. 2 (2023).

"Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection."

Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, and Philipp Zimmer. In The Web Conference 2023, 2023. Fast Company.

"The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization."

Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. In Proceedings of the 2022 ACM Conference on Economics and Computation, New York, NY: July 2022. Download Paper.

"Model Multiplicity: Opportunities, Concerns, and Solutions."

Emily Black, Manish Raghavan, and Solon Barocas. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, New York, NY: June 2022. Download Paper.

"Stochastic Model for Sunk Cost Bias."

Jon Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos, Erik Quaeghebeur, and Marloes H. Maathuis. Portland, Oregon: July 2021. Supplementary PDF. Download Paper.