The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement OptimizationJon 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 SolutionsEmily Black, Manish Raghavan, and Solon Barocas. FAccT 2022
Stochastic Model for Sunk Cost BiasJon Kleinberg, Sigal Oren, Manish Raghavan, and Nadav Sklar. UAI 2021
Algorithmic Monoculture and Social WelfareJon Kleinberg and Manish Raghavan. PNAS, May 2021
Bridging Machine Learning and Mechanism Design towards Algorithmic FairnessJessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, and Stratis Tsirtis. FAccT 2021
Roles for Computing in Social ChangeRediet 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 PracticesManish 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 ReasonsSolon 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 UncertaintyManish Raghavan, Manish Purohit, and Sreenivas Gollapudi. ICML 2019
The Externalities of Exploration and How Data Diversity Helps ExploitationManish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu. COLT 2018
For a version focused on the technical results, see Greedy Algorithm almost Dominates in Smoothed Contextual Bandits.
Mapping the Invocation Structure of Online Political InteractionManish Raghavan, Ashton Anderson, and Jon Kleinberg. WWW 2018
Selection Problems in the Presence of Implicit BiasJon Kleinberg and Manish Raghavan. ITCS 2018 [video]
On Fairness and CalibrationGeoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger. NeurIPS 2017
Planning with Multiple BiasesJon Kleinberg, Sigal Oren, and Manish Raghavan. EC 2017 [video]
Inherent Trade-Offs in the Fair Determination of Risk ScoresJon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. ITCS 2017 [video]
Press: Washington Post, ProPublica, Harvard Business Review