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Manish Raghavan
Manish Raghavan
MIT
Bestätigte E-Mail-Adresse bei mit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
23062017
On fairness and calibration
G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger
Advances in neural information processing systems 30, 2017
11242017
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
7402020
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
3122020
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
2672020
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
1852020
Selection problems in the presence of implicit bias
J Kleinberg, M Raghavan
9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2018
1222018
Model multiplicity: Opportunities, concerns, and solutions
E Black, M Raghavan, S Barocas
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022
1092022
Algorithmic monoculture and social welfare
J Kleinberg, M Raghavan
Proceedings of the National Academy of Sciences 118 (22), e2018340118, 2021
1062021
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
772021
The Externalities of Exploration and How Data Diversity Helps Exploitation
M Raghavan, A Slivkins, JW Vaughan, ZS Wu
Conference on Learning Theory, 2018
602018
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
592024
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
392016
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
352014
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
312021
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
302023
Planning with multiple biases
J Kleinberg, S Oren, M Raghavan
Proceedings of the 2017 ACM Conference on Economics and Computation, 567-584, 2017
302017
Human bias in algorithm design
CK Morewedge, S Mullainathan, HF Naushan, CR Sunstein, J Kleinberg, ...
Nature Human Behaviour 7 (11), 1822-1824, 2023
252023
Challenges for mitigating bias in algorithmic hiring
M Raghavan, S Barocas
< bound method Organization. get_name_with_acronym of< Organization …, 2019
252019
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
222023
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