Publications
Reconciling the accuracy-diversity trade-off in recommendations
Kenny Peng, Manish Raghavan, Emma Pierson, Jon Kleinberg, Nikhil Garg. Working paperSimplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer. WWW 2023Best Paper Award
Press: Fast Company
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. SIAM Journal on Computing, April 2023The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. EC 2022 [video]Exemplary Applied Modeling Track Paper
Related blog posts: Montreal AI Ethics Blog, MIT Initiative on the Digital Economy
Press: Cornell Chronicle
Model Multiplicity: Opportunities, Concerns, and Solutions
Emily Black, Manish Raghavan, and Solon Barocas. FAccT 2022Stochastic Model for Sunk Cost Bias
Jon Kleinberg, Sigal Oren, Manish Raghavan, and Nadav Sklar. UAI 2021Algorithmic Monoculture and Social Welfare
Jon Kleinberg and Manish Raghavan. PNAS, May 2021Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, and Stratis Tsirtis. FAccT 2021Roles for Computing in Social Change
Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G. Robinson. FAccT 2020 [video of Karen's presentation]Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. FAccT 2020 [video]Press: Communications of the ACM, MIT Technology Review, Fortune, Fortune (video), TechTarget, The Globe and Mail, HR Technologist, Cornell Chronicle
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan. FAccT 2020 [video of Solon's presentation]How Do Classifiers Induce Agents To Invest Effort Strategically?
Jon Kleinberg and Manish Raghavan. EC 2019 [video]Also appeared as:
Designing Evaluation Rules that are Robust to Strategic Behavior at the AAAI-20 Sister Conference Track.
How Do Classifiers Induce Agents to Invest Effort Strategically? in ACM Transactions on Economics and Computation, October 2020.
Algorithmic Classification and Strategic Effort in SIGecom Exchanges, November 2020.
Hiring Under Uncertainty
Manish Raghavan, Manish Purohit, and Sreenivas Gollupadi. ICML 2019The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. COLT 2018For more details on the technical results, see Greedy Algorithm almost Dominates in Smoothed Contextual Bandits.
Mapping the Invocation Structure of Online Political Interaction
Manish Raghavan, Ashton Anderson, and Jon Kleinberg. WWW 2018Selection Problems in the Presence of Implicit Bias
Jon Kleinberg and Manish Raghavan. ITCS 2018 [video]On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger. NeurIPS 2017Planning with Multiple Biases
Jon Kleinberg, Sigal Oren, and Manish Raghavan. EC 2017 [video]Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. ITCS 2017 [video]Press: Washington Post, ProPublica, Harvard Business Review