| 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 | 2782 | 2017 |
| On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in neural information processing systems 30, 2017 | 1328 | 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 | 1260 | 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 | 400 | 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 | 311 | 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 | 216 | 2020 |
| Algorithmic monoculture and social welfare J Kleinberg, M Raghavan Proceedings of the National Academy of Sciences 118 (22), e2018340118, 2021 | 181 | 2021 |
| Model multiplicity: Opportunities, concerns, and solutions E Black, M Raghavan, S Barocas Proceedings of the 2022 ACM conference on fairness, accountability, and …, 2022 | 169 | 2022 |
| Selection problems in the presence of implicit bias J Kleinberg, M Raghavan 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2018 | 142 | 2018 |
| 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 | 104 | 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 | 99 | 2024 |
| The Externalities of Exploration and How Data Diversity Helps Exploitation M Raghavan, A Slivkins, JW Vaughan, ZS Wu Conference on Learning Theory, 2018 | 71 | 2018 |
| Human bias in algorithm design CK Morewedge, S Mullainathan, HF Naushan, CR Sunstein, J Kleinberg, ... Nature Human Behaviour 7 (11), 1822-1824, 2023 | 48 | 2023 |
| 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 | 47 | 2023 |
| 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 | 45 | 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 | 37 | 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 | 36 | 2021 |
| The inversion problem: Why algorithms should infer mental state and not just predict behavior J Kleinberg, J Ludwig, S Mullainathan, M Raghavan Perspectives on Psychological Science 19 (5), 827-838, 2024 | 35 | 2024 |
| Planning with multiple biases J Kleinberg, S Oren, M Raghavan Proceedings of the 2017 ACM Conference on Economics and Computation, 567-584, 2017 | 35 | 2017 |
| Challenges for mitigating bias in algorithmic hiring M Raghavan, S Barocas Brookings. Retrieved February 25, 2020, 2019 | 34 | 2019 |
Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
