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Local Data Debiasing for Fairness Based on Generative Adversarial Training

Départment d’Informatique, Université du Québec à Montréal, Montreal, QC H2L 2C4, Canada
Laboratoire d’Informatique de l’École polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France
DIRO, Université de Montréal, Montreal, QC H3T 1J4, Canada
Author to whom correspondence should be addressed.
Academic Editor: Laurent Risser
Algorithms 2021, 14(3), 87;
Received: 31 December 2020 / Revised: 9 March 2021 / Accepted: 9 March 2021 / Published: 14 March 2021
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility. View Full-Text
Keywords: sanitization; fairness; generative adversarial network sanitization; fairness; generative adversarial network
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MDPI and ACS Style

Aïvodji, U.; Bidet, F.; Gambs, S.; Ngueveu, R.C.; Tapp, A. Local Data Debiasing for Fairness Based on Generative Adversarial Training. Algorithms 2021, 14, 87.

AMA Style

Aïvodji U, Bidet F, Gambs S, Ngueveu RC, Tapp A. Local Data Debiasing for Fairness Based on Generative Adversarial Training. Algorithms. 2021; 14(3):87.

Chicago/Turabian Style

Aïvodji, Ulrich, François Bidet, Sébastien Gambs, Rosin C. Ngueveu, and Alain Tapp. 2021. "Local Data Debiasing for Fairness Based on Generative Adversarial Training" Algorithms 14, no. 3: 87.

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