Differentially Private Fair Learning

Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi -Malvajerdi, Jonathan Ullman
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3000-3008, 2019.

Abstract

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. Our first algorithm is a private implementation of the equalized odds post-processing approach of (Hardt et al., 2016). This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of “disparate treatment”. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of (Agarwal et al., 2018) which is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-jagielski19a, title = {Differentially Private Fair Learning}, author = {Jagielski, Matthew and Kearns, Michael and Mao, Jieming and Oprea, Alina and Roth, Aaron and -Malvajerdi, Saeed Sharifi and Ullman, Jonathan}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {3000--3008}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/jagielski19a/jagielski19a.pdf}, url = {https://proceedings.mlr.press/v97/jagielski19a.html}, abstract = {Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. Our first algorithm is a private implementation of the equalized odds post-processing approach of (Hardt et al., 2016). This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of “disparate treatment”. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of (Agarwal et al., 2018) which is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.} }
Endnote
%0 Conference Paper %T Differentially Private Fair Learning %A Matthew Jagielski %A Michael Kearns %A Jieming Mao %A Alina Oprea %A Aaron Roth %A Saeed Sharifi -Malvajerdi %A Jonathan Ullman %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-jagielski19a %I PMLR %P 3000--3008 %U https://proceedings.mlr.press/v97/jagielski19a.html %V 97 %X Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. Our first algorithm is a private implementation of the equalized odds post-processing approach of (Hardt et al., 2016). This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of “disparate treatment”. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of (Agarwal et al., 2018) which is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.
APA
Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., -Malvajerdi, S.S. & Ullman, J.. (2019). Differentially Private Fair Learning. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:3000-3008 Available from https://proceedings.mlr.press/v97/jagielski19a.html.

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