Privacy for All: Ensuring Fair and Equitable Privacy Protections

Michael D. Ekstrand, Rezvan Joshaghani, Hoda Mehrpouyan

Research output: Contribution to journalConference articlepeer-review

55 Scopus citations

Abstract

In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and nondiscriminatory 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 thinking and research on privacy and fairness. 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.

Original languageEnglish
Pages (from-to)35-47
Number of pages13
JournalProceedings of Machine Learning Research
Volume81
StatePublished - 2018
Event1st Conference on Fairness, Accountability and Transparency, FAT* 2018 - New York, United States
Duration: 23 Feb 201824 Feb 2018

Keywords

  • Fairness
  • Privacy

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