Fairness with censorship and group constraints-Reference-Cited by-同舟云学术

Fairness with censorship and group constraints

Published:2023-03-02 Issue:6 Volume:65 Page:2571-2594
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Zhang Wenbin^ORCID,Weiss Jeremy C.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10115-023-01842-5.pdf

Reference71 articles.

1. Agarwal A, Beygelzimer A, Dudík M, Langford J, Wallach H (2018) A reductions approach to fair classification. In: international conference on machine learning, pp 60–69. PMLR

2. Angwin J, Larson J, Mattu S, Kirchner L (2016) There’s software used across the country to predict future criminals. ProPublica, and it’s biased against blacks

3. Barocas S, Hardt M, Narayanan A (2017) Fairness in machine learning. Nips Tutorial 1:2

4. Barocas S, Selbst AD (2016) Big data’s disparate impact. Calif Law Rev 104(3):671

5. Bechavod Y, Jung C, Wu SZ (2020) Metric-free individual fairness in online learning. Adv Neural Inform Proc Syst 33:11214

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