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).
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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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