| Big Data's Disparate Impact S Barocas, AD Selbst California Law Review 104 (3), 671-732, 2016 | 4586 | 2016 |
| Big Data's Disparate Impact S Barocas, AD Selbst California Law Review 104 (3), 671-732, 2016 | 4441 | 2016 |
| Fairness and Machine Learning: Limitations and Opportunities S Barocas, M Hardt, A Narayanan MIT Press, 2023 | 1816* | 2023 |
| Accountable Algorithms JA Kroll, J Huey, S Barocas, EW Felten, JR Reidenberg, DG Robinson, ... University of Pennsylvania Law Review 165, 633, 2016 | 1585 | 2016 |
| Language (Technology) is Power: A Critical Survey of "Bias" in NLP SL Blodgett, S Barocas, H Daumé III, H Wallach Conference of the Association for Computational Linguistics (ACL), 2020 | 1075 | 2020 |
| The Intuitive Appeal of Explainable Machines A Selbst, S Barocas Fordham Law Review, 2018 | 669 | 2018 |
| Algorithmic Fairness: Choices, Assumptions, and Definitions S Mitchell, E Potash, S Barocas, A D’Amour, K Lum Annual Review of Statistics and Its Application 8, 2021 | 646* | 2021 |
| Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices M Raghavan, S Barocas, J Kleinberg, K Levy Conference on Fairness, Accountability, and Transparency (FAccT), 2020 | 591 | 2020 |
| Adnostic: Privacy Preserving Targeted Advertising V Toubiana, A Narayanan, D Boneh, H Nissenbaum, S Barocas Proceedings of the Network and Distributed System Security Symposium (NDSS), 2010 | 486 | 2010 |
| Big Data’s End Run around Anonymity and Consent S Barocas, H Nissenbaum Privacy, Big Data, and the Public Good: Frameworks for Engagement, 44-75, 2014 | 459 | 2014 |
| Ten Simple Rules for Responsible Big Data Research M Zook, S Barocas, K Crawford, E Keller, SP Gangadharan, A Goodman, ... PLOS Computational Biology 13 (3), e1005399, 2017 | 357* | 2017 |
| AI Now 2017 Report A Campolo, M Sanfilippo, M Whittaker, K Crawford | 304* | 2017 |
| Roles for Computing in Social Change R Abebe, S Barocas, J Kleinberg, K Levy, M Raghavan, D Robinson Conference on Fairness, Accountability, and Transparency (FAccT), 2020 | 280 | 2020 |
| The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons S Barocas, A Selbst, M Raghavan Conference on Fairness, Accountability, and Transparency (FAccT), 2020 | 248 | 2020 |
| Discriminating Tastes: Uber's Customer Ratings as Vehicles for Workplace Discrimination A Rosenblat, KEC Levy, S Barocas, T Hwang Policy & Internet, 2017 | 242* | 2017 |
| Governing Algorithms: A Provocation Piece S Barocas, S Hood, M Ziewitz Governing Algorithms: A Conference on Computation, Automation, and Control, 2013 | 217* | 2013 |
| Problem Formulation and Fairness S Passi, S Barocas Conference on Fairness, Accountability, and Transparency (FAccT), 2019 | 211 | 2019 |
| Big Data's End Run around Procedural Privacy Protections S Barocas, H Nissenbaum Communications of the ACM 57 (11), 31-33, 2014 | 186 | 2014 |
| The Problem with Bias: From Allocative to Representational Harms in Machine Learning S Barocas, K Crawford, A Shapiro, H Wallach Special Interest Group for Computing, Information, and Society (SIGCIS), 2017 | 179* | 2017 |
| Principles for Accountable Algorithms and a Social Impact Statement for Algorithms N Diakopoulos, S Friedler, M Arenas, S Barocas, M Hay, B Howe, ... Fairness, Accountability, and Transparency in Machine Learning, 2016 | 129* | 2016 |
Arvind NarayananAssociate Professor, Princeton University在 cs.princeton.edu 的电子邮件经过验证
