Beyond Reasonable Doubt-Reference-Cited by-同舟云学术

Beyond Reasonable Doubt

Published:2021-07-21 Issue: Volume: Page:
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Container-title:Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
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Author:

Bakker Michiel A.¹,Tu Duy Patrick²,Gummadi Krishna P.³,Pentland Alex Sandy⁴,Varshney Kush R.⁵,Weller Adrian⁶

Affiliation:

1. Massachusetts Institute of Technology & MIT-IBM Watson AI Lab, Cambridge, MA, USA

2. Massachusetts Institute of Technology & MIT-IBM Watson AI Lab, Cambridge, MA, Germany

3. Max Planck Institute for Software Systems, Saarbrücken, Germany

4. Massachusetts Institute of Technology & MIT-IBM Watson AI Lab, Cambrdige, MA, USA

5. IBM Research, Yorktown Heights, NY, USA

6. University of Cambridge & The Alan Turing Institute, Cambrdige, United Kingdom

Funder

Engineering and Physical Sciences Research Council

Leverhulme Trust

Turing AI Fellowship

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3461702.3462575

Reference40 articles.

1. rivacyAono, Y.; Hayashi, T.; Wang, L.; Moriai, S.; et al. 2017. Privacy-preserving deep learning via additively homomorphic encryption;IEEE Transactions on Information Forensics and Security,2017

2. bechavod2017penalizingBechavod Y.; and Ligett K. 2017. Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044 . bechavod2017penalizingBechavod Y.; and Ligett K. 2017. Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044 .

3. berk2018fairnessBerk R.; Heidari H.; Jabbari S.; Kearns M.; and Roth A. 2018. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research 0049124118782533. berk2018fairnessBerk R.; Heidari H.; Jabbari S.; Kearns M.; and Roth A. 2018. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research 0049124118782533.

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