Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias

Author:

Wyllie Sierra1ORCID,Shumailov Ilia2ORCID,Papernot Nicolas1ORCID

Affiliation:

1. University of Toronto and Vector Institute, Canada

2. University of Oxford, United Kingdom

Funder

Microsoft

NSERC

CIFAR

Publisher

ACM

Reference74 articles.

1. 90th United States Congress. 1968. 82 Stat. 73 - An Act to prescribe penalties for certain acts of violence or intimidation and for other purposes. https://www.hud.gov/sites/dfiles/FHEO/documents/fairhousingact.pdf

2. Federal Housing Administration. 1938. Underwriting Manual: Underwriting and Valuation Procedure Under Title 2 of the National Housing Act. Department of Housing and Urban Development. https://www.huduser.gov/portal/sites/default/files/pdf/Federal-Housing-Administration-Underwriting-Manual.pdf

3. Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp. 2019. Fairwashing: the risk of rationalization. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 161–170. https://proceedings.mlr.press/v97/aivodji19a.html

4. Sina Alemohammad Josue Casco-Rodriguez Lorenzo Luzi Ahmed Imtiaz Humayun Hossein Babaei Daniel LeJeune Ali Siahkoohi and Richard G. Baraniuk. 2023. Self-Consuming Generative Models Go MAD. arxiv:2307.01850 [cs.LG] https://arxiv.org/abs/2307.01850

5. Martin Arjovsky Léon Bottou Ishaan Gulrajani and David Lopez-Paz. 2020. Invariant Risk Minimization. arxiv:1907.02893 [stat.ML] https://arxiv.org/abs/1907.02893

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