Active Fairness in Algorithmic Decision Making-Reference-Cited by-同舟云学术

Active Fairness in Algorithmic Decision Making

Published:2019-01-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
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Author:

Noriega-Campero Alejandro¹,Bakker Michiel A.¹,Garcia-Bulle Bernardo²,Pentland Alex 'Sandy'¹

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. Mexican Institute of Technology, Cambridge, MA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3306618.3314277

Reference29 articles.

1. Bechavod Y. and Ligett K. 2017. Learning fair classifiers: A regularization-inspired approach.arXiv preprint. Bechavod Y. and Ligett K. 2017. Learning fair classifiers: A regularization-inspired approach.arXiv preprint.

2. Building Classifiers with Independency Constraints

3. Chalfin A.; Danieli O.; Hillis A.; Jelveh Z.; Luca M.; Ludwig J.; and Mullainathan S. 2016. Productivity and selection of human capital with machine learning. American Economic Review 106(5). Chalfin A.; Danieli O.; Hillis A.; Jelveh Z.; Luca M.; Ludwig J.; and Mullainathan S. 2016. Productivity and selection of human capital with machine learning. American Economic Review 106(5).

4. Corbett-Davies S. and Goel S. 2018. The measure and mismeasure of fairness: A critical review of fair machine learning.arXiv preprint arXiv:1808.00023. Corbett-Davies S. and Goel S. 2018. The measure and mismeasure of fairness: A critical review of fair machine learning.arXiv preprint arXiv:1808.00023.

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