The interrelation between data and AI ethics in the context of impact assessments
Open accessAbstract
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.
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Data Protection Impact Assessments (DPIA) are the most well-established form of auditing and impact assessment.
“The most well-established form of this is Data Protection Impact Assessments (DPIA)69,70”
From A high-level overview of AI ethics (2021) · cites this paper as [70]
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 2× · see citing works
“Indeed, what is now referred to as ‘AI ethics’ or ‘trustworthy AI’ or ‘responsible AI’ is the body of literature that has resulted because of this consciousness and debate [14].”
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
A high-level overview of AI ethics
Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269 · see citing works
Self-citationLo Piano, S. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0501-9 · Cited by 229 · cited 1× · see citing works
“The first was an AI ethics set of principles [15].”
On Human Dignity as a Foundation for the Right to Privacy
Floridi, L. (2016) · Philosophy & Technology · doi:10.1007/s13347-016-0220-8 · Cited by 190 · see citing works
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Thinking responsibly about responsible AI and ‘the dark side’ of AI
Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2026621 · Cited by 435 · see citing works
A high-level overview of AI ethics
Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269 · see citing works
Self-citationAcademic Integrity and Students’ Ethical Use of ChatGPT in Higher Education
Shishakly, R., & Nachouki, M. (2026) · Journal of Information Technology Education: Research · doi:10.28945/5730 · Cited by 4 · see citing works
“Nevertheless, transparency remains in-consistently embedded in institutional policy frameworks, which are fragmented and still evolving (Jobin et al., 2019; Kazim & Koshiyama, 2021; Larsson & Heintz, 2020).”
- Authors
- Kazim, E., & Koshiyama, A.
- Affiliations
- University College London (GB)
- Corresponding author
- Kazim, Emre
- Venue
- AI and Ethics
- Published
- 2020-11-22
- Volume
- 1
- Issue
- 3
- Pages
- 219-225
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W3092960325
- Cited by
- 39
- References
- 7
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 5 · 2023: 4 · 2022: 11 · 2021: 7 · 2020: 1
- FWCI
- 7.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI and Ethics (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Funders
- University College London
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- AI ethics, data protection impact assessment, AI impact assessment, data protection, ethical principles, value trade-offs, privacy, fairness
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