Big Data's Disparate Impact S Barocas, AD Selbst California Law Review 104 (3), 671-732, 2016 | 4529 | 2016 |
Fairness and Abstraction in Sociotechnical Systems AD Selbst, danah boyd, S Friedler, S Venkatasubramanian, J Vertesi ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2018 | 1042 | 2018 |
The Intuitive Appeal of Explainable Machines AD Selbst, S Barocas Fordham Law Review 87, 1085-1139, 2018 | 630 | 2018 |
Meaningful Information and the Right to Explanation’(2017) AD Selbst, J Powles International Data Privacy Law 7, 233, 0 | 609* | |
Disparate Impact in Big Data Policing AD Selbst Georgia Law Review 52, 109-195, 2017 | 452 | 2017 |
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 | 245 | 2020 |
The fallacy of AI functionality ID Raji, IE Kumar, A Horowitz, A Selbst Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 165 | 2022 |
Negligence and AI's Human Users AD Selbst Boston University Law Review 100, 1315-1376, 2020 | 161 | 2020 |
An Institutional View of Algorithmic Impact Assessments AD Selbst Harvard Journal of Law & Technology 35 (1), 2021 | 93 | 2021 |
Contextual Expectations of Privacy AD Selbst Cardozo Law Review 35, 643-709, 2013 | 61 | 2013 |
Unfair Artificial Intelligence: How FTC Intervention Can Overcome the Limitations of Discrimination Law AD Selbst, S Barocas University of Pennsylvania Law Review 171, 1023-1093, 2023 | 22 | 2023 |
A Mild Defense of Our New Machine Overlords AD Selbst Vanderbilt Law Review En Banc 70, 87-104, 2017 | 14 | 2017 |
Deconstructing design decisions: Why courts must interrogate machine learning and other technologies AD Selbst, S Venkatasubramanian, IE Kumar Ohio State Law Journal, 23-22, 2024 | 6* | 2024 |
The Journalism Ratings Board: An Incentive-Based Approach to Cable News Accountability A Selbst U. Mich. JL Reform 44, 467, 2010 | 5 | 2010 |
Clock division as a power saving strategy in a system constrained by high transmission frequency and low data rate AD Selbst Massachusetts Institute of Technology, 2005 | 4 | 2005 |
Angwin, Julia ua: Machine Bias. There’s software used across the country to predict future criminals. And it’s biased against blacks, ProPublica 2016, abrufbar unter:< www … W Aspray, S Barocas, AD Selbst, AM Barry-Jester, B Casselman, ... | | |
LAW & TECHNOLOGY D Karshtedt, MA Lemley, SB Seymore, AD Selbst, CYN Smith | | |
HARVARD JOURNAL OF LAW & TECHNOLOGY D Karshtedt, MA Lemley, SB Seymore, AD Selbst, CYN Smith | | |