Practical fundamental rights impact assessments
Open accessAbstract
The European Union’s General Data Protection Regulation tasks organizations to perform a Data Protection Impact Assessment (DPIA) to consider fundamental rights risks of their artificial intelligence (AI) system. However, assessing risks can be challenging, as fundamental rights are often considered abstract in nature. So far, guidance regarding DPIAs has largely focussed on data protection, leaving broader fundamental rights aspects less elaborated. This is problematic because potential negative societal consequences of AI systems may remain unaddressed and damage public trust in organizations using AI. Towards this, we introduce a practical, four-Phased framework, assisting organizations with performing fundamental rights impact assessments. This involves organizations (i) defining the system’s purposes and tasks, and the responsibilities of parties involved in the AI system; (ii) assessing the risks regarding the system’s development; (iii) justifying why the risks of potential infringements on rights are proportionate; and (iv) adopt organizational and/or technical measures mitigating risks identified. We further indicate how regulators might support these processes with practical guidance.
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FRIA scholarship and official fundamental-rights research informed the assessment architecture.
“FRIA scholarship and official fundamental-rights research informed the assessment architecture(Bertaina et al., 2025; European Union Agency for Fundamental Rights, 2025;Janssen et al., 2022;Mantelero, 2024;Thomaidou & Limniotis, 2025).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as Janssen et al., 2022
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Research works printed but not linked: 5
- A Adadi and M Berrada, 'Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence' (2018) IEEE Access 52138-52160 <https://ieeexplore.ieee.org/abstract/document/8466590> accessed 18 May 2022.
- J Singh and others, 'Responsibility & Machine Learning: Part of a Process' (27 October 2016) <https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=2860048> accessed 18 May 2022.
- M Seng Ah Lee and J Singh, 'Risk Identification Questionnaire for Unintended Bias in Machine Learning Development Lifecycle' ( July 2021), in Proceedings of ACM Conference on AI, Ethics, and Society 704.
- J Singh, J Cobbe and C Norval, 'Decision Provenance: Harnessing Data Flow for Accountable Systems' (2018) 7 IEEE 6562.
- J. Cobbe, M. Seng Ah Lee, J Singh, 'Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems' (17 December 2020), in ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), 1-10 March 2021, Virtual Event, Canada <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3772964> accessed 18 May 2022.
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- Authors
- Janssen, H., Seng Ah Lee, M., & Singh, J.
- Affiliations
- University of Cambridge (GB)
- Compliant & Accountable Systems Research Group, Department of Computer Science and Technology, University of Cambridge , United Kingdom
- Corresponding author
- Janssen, Heleen
- Venue
- International Journal of Law and Information Technology
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-06-01
- Volume
- 30
- Issue
- 2
- Pages
- 200-232
- ISSN
- 0967-0769 (ISSN-L)
- 1464-3693
- OpenAlex
- W4309504924
- Cited by
- 31
- References
- 0
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 10 · 2023: 1 · 2015: 1
- FWCI
- 6.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Law and Information Technology (publishedVersion, cc-by)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- UK Engineering & Physical Sciences Research Council (EP/R033501/1, EP/P024394/1)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- fundamental rights impact assessment, data protection impact assessment, artificial intelligence, General Data Protection Regulation, AI risk assessment, proportionality, risk mitigation, AI regulation, public trust in AI, regulatory guidelines
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