Manish Raghavan

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Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Sendhil MullainathanMITVerified email at mit.edu
Solon BarocasPrincipal Researcher, Microsoft Research; Adjunct Associate Professor, Information Science, CornellVerified email at cornell.edu
Karen LevyCornell UniversityVerified email at cornell.edu
Geoff PleissAssistant Professor, University of British ColumbiaVerified email at stat.ubc.ca
Felix WuGoogle DeepMindVerified email at google.com
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Aleksandrs SlivkinsSenior Principal Researcher, Microsoft Research NYCVerified email at microsoft.com
David G. RobinsonOpenAIVerified email at robinsonian.com
Rediet AbebeHarvard Society of FellowsVerified email at fas.harvard.edu
Sigal OrenBen Gurion UniversityVerified email at cs.bgu.ac.il
Andrew SelbstUCLA School of LawVerified email at law.ucla.edu
Rohan AlurPhD Student at MITVerified email at mit.edu
Gourab K. PatroResearch Scientist @ Quantiphi | Previously @ L3S, IIT Kharagpur, MPI-SWS, IIT JodhpurVerified email at quantiphi.com
Ana-Andreea StoicaResearch Group Leader, Max Planck Institute for Intelligent SystemsVerified email at columbia.edu
Roland MaioPhD Student at Columbia UniversityVerified email at columbia.edu
Faidra MonachouAssistant Professor, Yale School of ManagementVerified email at yale.edu
Stratis TsirtsisMax Planck Institute for Software SystemsVerified email at mpi-sws.org

Manish Raghavan

MIT

Verified email at mit.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Inherent trade-offs in the fair determination of risk scores J Kleinberg, S Mullainathan, M Raghavan Innovations in Theoretical Computer Science 67, 43:1--43:23, 2017	2314	2017
On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in neural information processing systems 30, 2017	1126	2017
Mitigating bias in algorithmic hiring: Evaluating claims and practices M Raghavan, S Barocas, J Kleinberg, K Levy Proceedings of the 2020 conference on fairness, accountability, and …, 2020	748	2020
Roles for computing in social change R Abebe, S Barocas, J Kleinberg, K Levy, M Raghavan, DG Robinson Proceedings of the 2020 conference on fairness, accountability, and …, 2020	315	2020
The hidden assumptions behind counterfactual explanations and principal reasons S Barocas, AD Selbst, M Raghavan Proceedings of the 2020 conference on fairness, accountability, and …, 2020	271	2020
How do classifiers induce agents to invest effort strategically? J Kleinberg, M Raghavan ACM Transactions on Economics and Computation (TEAC) 8 (4), 1-23, 2020	186	2020
Selection problems in the presence of implicit bias J Kleinberg, M Raghavan 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2018	122	2018
Model multiplicity: Opportunities, concerns, and solutions E Black, M Raghavan, S Barocas Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022	109	2022
Algorithmic monoculture and social welfare J Kleinberg, M Raghavan Proceedings of the National Academy of Sciences 118 (22), e2018340118, 2021	107	2021
Bridging machine learning and mechanism design towards algorithmic fairness J Finocchiaro, R Maio, F Monachou, GK Patro, M Raghavan, AA Stoica, ... Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021	77	2021
The challenge of understanding what users want: Inconsistent preferences and engagement optimization J Kleinberg, S Mullainathan, M Raghavan Management science 70 (9), 6336-6355, 2024	60	2024
The Externalities of Exploration and How Data Diversity Helps Exploitation M Raghavan, A Slivkins, JW Vaughan, ZS Wu Conference on Learning Theory, 2018	60	2018
Planning problems for sophisticated agents with present bias J Kleinberg, S Oren, M Raghavan Proceedings of the 2016 ACM Conference on Economics and Computation, 343-360, 2016	39	2016
Deduplicating a places database N Dalvi, M Olteanu, M Raghavan, P Bohannon Proceedings of the 23rd international conference on World wide web, 409-418, 2014	35	2014
Fairness on the ground: Applying algorithmic fairness approaches to production systems C Bakalar, R Barreto, S Bergman, M Bogen, B Chern, S Corbett-Davies, ... arXiv preprint arXiv:2103.06172, 2021	31	2021
Simplistic collection and labeling practices limit the utility of benchmark datasets for Twitter bot detection C Hays, Z Schutzman, M Raghavan, E Walk, P Zimmer Proceedings of the ACM web conference 2023, 3660-3669, 2023	30	2023
Planning with multiple biases J Kleinberg, S Oren, M Raghavan Proceedings of the 2017 ACM Conference on Economics and Computation, 567-584, 2017	30	2017
Human bias in algorithm design CK Morewedge, S Mullainathan, HF Naushan, CR Sunstein, J Kleinberg, ... Nature Human Behaviour 7 (11), 1822-1824, 2023	26	2023
Challenges for mitigating bias in algorithmic hiring M Raghavan, S Barocas < bound method Organization. get_name_with_acronym of< Organization …, 2019	25	2019
Greedy algorithm almost dominates in smoothed contextual bandits M Raghavan, A Slivkins, JW Vaughan, ZS Wu SIAM Journal on Computing 52 (2), 487-524, 2023	22	2023

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