Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information-Reference-Cited by-同舟云学术

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Published:2021-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Awasthi Pranjal¹,Beutel Alex²,Kleindessner Matthäus³,Morgenstern Jamie⁴,Wang Xuezhi²

Affiliation:

1. Rutgers University & Google

2. Google

3. Amazon

4. University of Washington & Google

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442188.3445884

Reference32 articles.

1. Fairness in Recommendation Ranking through Pairwise Comparisons

2. Avrim Blum Suriya Gunasekar Thodoris Lykouris and Nati Srebro. 2018. On preserving non-discrimination when combining expert advice. In Advances in Neural Information Processing Systems. Avrim Blum Suriya Gunasekar Thodoris Lykouris and Nati Srebro. 2018. On preserving non-discrimination when combining expert advice. In Advances in Neural Information Processing Systems.

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