Fairness of Machine Learning in Search Engines-Reference-Cited by-同舟云学术

Fairness of Machine Learning in Search Engines

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Fang Yi¹,Liu Hongfu²,Tao Zhiqiang³,Yurochkin Mikhail⁴

Affiliation:

1. Santa Clara University, Santa Clara, CA, USA

2. Brandeis University, Waltham, MA, USA

3. Rochester Institute of Technology, Rochester, NY, USA

4. IBM Research & MIT-IBM Watson AI lab, Cambridge, MA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557501

Reference64 articles.

1. Alekh Agarwal Alina Beygelzimer Miroslav Dud'ik John Langford and Hanna Wallach. 2018. A reductions approach to fair classification. In ICML. Alekh Agarwal Alina Beygelzimer Miroslav Dud'ik John Langford and Hanna Wallach. 2018. A reductions approach to fair classification. In ICML.

2. Arturs Backurs Piotr Indyk Krzysztof Onak Baruch Schieber Ali Vakilian and Tal Wagner. 2019. Scalable fair clustering. In ICML. Arturs Backurs Piotr Indyk Krzysztof Onak Baruch Schieber Ali Vakilian and Tal Wagner. 2019. Scalable fair clustering. In ICML.

3. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

4. Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination

5. Alex Beutel , Jilin Chen , Tulsee Doshi , Hai Qian , Li Wei , Yi Wu , Lukasz Heldt , Zhe Zhao , Lichan Hong , Ed H Chi, et al . 2019 . Fairness in recommendation ranking through pairwise comparisons. In SIGKDD. Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al. 2019. Fairness in recommendation ranking through pairwise comparisons. In SIGKDD.

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