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The interrelation between data and AI ethics in the context of impact assessments

Kazim, E; Koshiyama, A; (2020) The interrelation between data and AI ethics in the context of impact assessments. AI and Ethics 10.1007/s43681-020-00029-w. (In press). Green open access

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Abstract

In the growing literature on artificial intelligence (AI) impact assessments, the literature on data protection impact assessments is heavily referenced. Given the relative maturity of the data protection debate and that it has translated into legal codification, it is indeed a natural place to start for AI. In this article, we anticipate directions in what we believe will become a dominant and impactful forthcoming debate, namely, how to conceptualise the relationship between data protection and AI impact. We begin by discussing the value canvas i.e. the ethical principles that underpin data and AI ethics, and discuss how these are instantiated in the context of value trade-offs when the ethics are applied. Following this, we map three kinds of relationships that can be envisioned between data and AI ethics, and then close with a discussion of asymmetry in value trade-offs when privacy and fairness are concerned.

Type: Article
Title: The interrelation between data and AI ethics in the context of impact assessments
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s43681-020-00029-w
Publisher version: https://doi.org/10.1007/s43681-020-00029-w
Language: English
Additional information: © 2021 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Artificial intelligence, Machine learning, Data, Data protection, Ethics, Audit, Impact assessment
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10126723
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