When Fair Classification Meets Noisy Protected Attributes

Author:

Ghosh Avijit1ORCID,Kvitca Pablo2ORCID,Wilson Christo1ORCID

Affiliation:

1. Khoury College of Computer Sciences, Northeastern University, USA

2. Khoury College of Computer Sciences, Northeastern University, USA and Amazon, USA

Publisher

ACM

Reference65 articles.

1. 2014. Using publicly available information to proxy for unidentified race and ethnicity: A methodology and assessment . Consumer Financial Protection Bureau . https://files.consumerfinance.gov/f/ 2014 09_cfpb_report_proxy-methodology.pdf 2014. Using publicly available information to proxy for unidentified race and ethnicity: A methodology and assessment. Consumer Financial Protection Bureau. https://files.consumerfinance.gov/f/201409_cfpb_report_proxy-methodology.pdf

2. Alekh Agarwal , Alina Beygelzimer , Miroslav Dudík , John Langford , and Hanna Wallach . 2018 . A reductions approach to fair classification . In International Conference on Machine Learning. PMLR, 60–69 . Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International Conference on Machine Learning. PMLR, 60–69.

3. Alekh Agarwal , Miroslav Dudík , and Zhiwei Steven Wu . 2019 . Fair regression: Quantitative definitions and reduction-based algorithms . In International Conference on Machine Learning. PMLR, 120–129 . Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu. 2019. Fair regression: Quantitative definitions and reduction-based algorithms. In International Conference on Machine Learning. PMLR, 120–129.

4. Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

5. What We Can't Measure, We Can't Understand

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