Model Debiasing via Gradient-based Explanation on Representation

Author:

Zhang Jindi1ORCID,Wang Luning1ORCID,Su Dan2ORCID,Huang Yongxiang1ORCID,Cao Caleb Chen3ORCID,Chen Lei3ORCID

Affiliation:

1. Hong Kong Research Center, Huawei, Hong Kong

2. NVIDIA Research, Hong Kong

3. Hong Kong University of Science and Technology, Hong Kong

Publisher

ACM

Reference36 articles.

1. Toon Calders and Sicco Verwer . 2010. Three naive bayes approaches for discrimination-free classification. Data mining and knowledge discovery 21, 2 ( 2010 ), 277–292. Toon Calders and Sicco Verwer. 2010. Three naive bayes approaches for discrimination-free classification. Data mining and knowledge discovery 21, 2 (2010), 277–292.

2. Ricky  TQ Chen , Xuechen Li , Roger Grosse , and David Duvenaud . 2018. Isolating sources of disentanglement in variational autoencoders. arXiv preprint arXiv:1802.04942 ( 2018 ). Ricky TQ Chen, Xuechen Li, Roger Grosse, and David Duvenaud. 2018. Isolating sources of disentanglement in variational autoencoders. arXiv preprint arXiv:1802.04942 (2018).

3. Elliot Creager David Madras Jörn-Henrik Jacobsen Marissa Weis Kevin Swersky Toniann Pitassi and Richard Zemel. 2019. Flexibly fair representation learning by disentanglement. In ICML. PMLR 1436–1445. Elliot Creager David Madras Jörn-Henrik Jacobsen Marissa Weis Kevin Swersky Toniann Pitassi and Richard Zemel. 2019. Flexibly fair representation learning by disentanglement. In ICML. PMLR 1436–1445.

4. Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility

5. Fairness is not static

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