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Some HCI Priorities for GDPR-Compliant Machine Learning

Author

Listed:
  • Veale, Michael

    (University College London)

  • Binns, Reuben
  • Van Kleek, Max

Abstract

Cite as Michael Veale, Reuben Binns and Max Van Kleek (2018) Some HCI Priorities for GDPR-Compliant Machine Learning. The General Data Protection Regulation: An Opportunity for the CHI Community? (CHI-GDPR 2018), Workshop at ACM CHI'18, 22 April 2018, Montreal, Canada. In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing.

Suggested Citation

  • Veale, Michael & Binns, Reuben & Van Kleek, Max, 2018. "Some HCI Priorities for GDPR-Compliant Machine Learning," LawArchive wm6yk_v1, Center for Open Science.
    • Handle: RePEc:osf:lawarc:wm6yk_v1
    DOI: 10.31219/osf.io/wm6yk_v1
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    References listed on IDEAS

    as
    1. Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawRxiv 97upg, Center for Open Science.
    2. Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.
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