Privacy for All: Ensuring Fair and Equitable Privacy Protections

Michael D. Ekstrand, Rezvan Joshaghani, Hoda Mehrpouyan
Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:35-47, 2018.

Abstract

In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and non-discriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate discrimination, so also privacy regimes may disproportionately fail to protect vulnerable members of their target population, resulting in disparate impact with respect to the effectiveness of privacy protections.We propose a research agenda that will illuminate this issue, along with related issues in the intersection of fairness and privacy, and present case studies that show how the outcomes of this research may change existing privacy and fairness research. We believe it is important to ensure that technologies and policies intended to protect the users and subjects of information systems provide such protection in an equitable fashion.

Cite this Paper

BibTeX
@InProceedings{pmlr-v81-ekstrand18a, title = {Privacy for All: Ensuring Fair and Equitable Privacy Protections}, author = {Ekstrand, Michael D. and Joshaghani, Rezvan and Mehrpouyan, Hoda}, booktitle = {Proceedings of the 1st Conference on Fairness, Accountability and Transparency}, pages = {35--47}, year = {2018}, editor = {Friedler, Sorelle A. and Wilson, Christo}, volume = {81}, series = {Proceedings of Machine Learning Research}, month = {23--24 Feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v81/ekstrand18a/ekstrand18a.pdf}, url = {https://proceedings.mlr.press/v81/ekstrand18a.html}, abstract = {In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and non-discriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate discrimination, so also privacy regimes may disproportionately fail to protect vulnerable members of their target population, resulting in disparate impact with respect to the effectiveness of privacy protections.We propose a research agenda that will illuminate this issue, along with related issues in the intersection of fairness and privacy, and present case studies that show how the outcomes of this research may change existing privacy and fairness research. We believe it is important to ensure that technologies and policies intended to protect the users and subjects of information systems provide such protection in an equitable fashion.} }
Endnote
%0 Conference Paper %T Privacy for All: Ensuring Fair and Equitable Privacy Protections %A Michael D. Ekstrand %A Rezvan Joshaghani %A Hoda Mehrpouyan %B Proceedings of the 1st Conference on Fairness, Accountability and Transparency %C Proceedings of Machine Learning Research %D 2018 %E Sorelle A. Friedler %E Christo Wilson %F pmlr-v81-ekstrand18a %I PMLR %P 35--47 %U https://proceedings.mlr.press/v81/ekstrand18a.html %V 81 %X In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and non-discriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate discrimination, so also privacy regimes may disproportionately fail to protect vulnerable members of their target population, resulting in disparate impact with respect to the effectiveness of privacy protections.We propose a research agenda that will illuminate this issue, along with related issues in the intersection of fairness and privacy, and present case studies that show how the outcomes of this research may change existing privacy and fairness research. We believe it is important to ensure that technologies and policies intended to protect the users and subjects of information systems provide such protection in an equitable fashion.
APA
Ekstrand, M.D., Joshaghani, R. & Mehrpouyan, H.. (2018). Privacy for All: Ensuring Fair and Equitable Privacy Protections. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in Proceedings of Machine Learning Research 81:35-47 Available from https://proceedings.mlr.press/v81/ekstrand18a.html.

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