Regulating transparency: a critique of the “free and open source AI” definition in the EU AI act
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · cited 2× · see citing works
“Without mandatory disclosure, both regimes can be circumvented, since existing methods (such as model cards) cannot guarantee complete transpar - ency as their accuracy ultimately depends on the integrity of their creators [ 18 ].”
Wulf, A. J., & Seizov, O. (2022) · AI & Society · doi:10.1007/s00146-022-01424-z · Cited by 101 · see citing works
Open Data in SciencePreprint
Murray‐Rust, P. (2008) · Nature Precedings · doi:10.1038/npre.2008.1526.1 · Cited by 81 · see citing works
Rethinking open source generative AI: open washing and the EU AI Act
Liesenfeld, A., & Dingemanse, M. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659005 · Cited by 67 · cited 1× · see citing works
“As noted in Liesenfeld and Dingemanse (2024), t his multi - dimensional understanding is also essential when evaluating whether an AI system upholds the core Four Freedoms of ’free’ software: the rights to use, study, modify, and redis - tribute [ 12 ].”
Ruschemeier, H. (2023) · ERA Forum · doi:10.1007/s12027-022-00725-6 · Cited by 60 · see citing works
Wulf, A. J., & Seizov, O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4478311 · Cited by 15 · see citing works
Open-Source AI Made in the EU: Why it is a Good Idea
Floridi, L., Buttaboni, C., Hine, E., Novelli, C., Schröder, T., & Shanklin, G. (2025) · Minds and Machines · doi:10.1007/s11023-025-09728-x · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- M. A. O. B. Juan-Antonio Cordero-Fuertes, A. Anne and D. Natalia: EU digital technologies and policy conference (EUDTP 2025) abstracts and contributions. Brussels (2025)
- Bandy, J., Vincent, N.: Addressing ‘documentation debt’ in machine learning research: a retrospective datasheet for book corpus. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT) (2021)
- Aleksandra, P., Christopher, A., Paulo, V., Hugo, L., Gérard, D., Sasha, L., Yacine, J., Anna, R.: The {ROOTS} search tool: data transparency for {LLM}s. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 3: System Demonstrations, Toronto (2023)
- Authors
- Ramos, S., & Pianese, F.
- Affiliations
- Sorbonne Université (FR)
- Nokia (France) (FR)
- Corresponding author
- Ramos, Simona
- ORCID
- Ramos, Simona
- Venue
- AI and Ethics
- Published
- 2026-05-23
- Volume
- 6
- Issue
- 3
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W7162216914
- Semantic Scholar
- 0611d65ea07587c39894fb211be62180b2ce737d
- Cited by
- 0
- References
- 6
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- AI and Ethics (publishedVersion)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- EU AI Act, AI transparency, free and open source software, four freedoms, accountability, AI regulation, high-risk AI systems, privacy, intellectual property, fairness
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