The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The EU AI Liability Directive (AILD): Bridging Information Gaps
Open accessAbstract
The proposed European AI Liability Directive (AILD) is an important step towards closing the ‘liability gap’, i.e., the difficulty in assigning responsibility for harms caused by AI systems. However, if victims are to bring liability claims, they must first have ways of knowing that they have been subject to algorithmic discrimination or other harms caused by AI systems. This ‘information gap’ must be addressed if the AILD is to meet its regulatory objectives. In this article, we argue that the current version of the AILD reduces legal fragmentation but not legal uncertainty; privileges transparency and disclosure of evidence of high-risk systems over knowledge of harm and discrimination; and shifts the burden on the claimant from proving fault to accessing and understanding the evidence provided by the defendant. We conclude by providing four recommendations on how to improve the AILD to address the ‘liability gap’ and the ‘information gap’.
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Academic analysis concluded that while the AI Liability Directive reduced legal fragmentation, it did not resolve legal uncertainty, effectively shifting rather than eliminating the evidentiary burden on claimants.
“Academic analysis of the proposal similarly concluded that while the Directive reduced legal fragmentation, it did not resolve legal uncertainty, effectively shifting rather than eliminating the evidentiary burden on claimants (Ziosi et al. 2023).”
From Allocating Responsibility in Autonomous AI Systems: A Tiered Governance Model Under EU Regulation (2026) · cites this paper as Ziosi et al. 2023
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Transparency and reproducibility in artificial intelligence
Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature · doi:10.1038/s41586-020-2766-y · Cited by 540
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Research works printed but not linked: 16
- L Bertuzzi, 2022, The new liability rules for AI, Www.Euractiv.Com
- L Bertuzzi, 2023, MEPs seal the deal on Artificial Intelligence Act, Www.Euractiv.Com
- R Bommasani, 2022, On the Opportunities and Risks of Foundation Models
- 2022, 1_1_197605_prop_dir_ai_en.pdf Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on liability for defective products, DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on adapting non-contractual civil liability rules to artificial intelligence (AI Liability Directive)
- 2021, Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS
- European Parliament, 2023, Compromise Amendments on the Draft Report-Proposal for a regulation of the European Parliament and of the Council on harmonised rules on Artificial Intelligence (Artificial Governing AI safety through independent audits, Nature Machine Intelligence, 3
- C Ferguson, 2022, The Regulation of Artificial Intelligence in Canada and Abroad: Comparing the Proposed AIDA and EU AI Act
- P Hacker, 2022, The European AI Liability Directives -Critique of a Half-Hearted Approach and Lessons for the Future, SSRN Electronic Journal
- I Hupont, 2022, The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systems, Scientific Reports, 12, 10.1038/s41598-022-14981-6 · DOI
- J M�kander, 2021, Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations, Science and Engineering Ethics, 27
- S A Nawaz, 2022, The Proposed EU AI Liability Rules: Ease or Burden? European Law Blog
- Z Porter, 2022, Distinguishing two features of accountability for AI technologies, Nature Machine Intelligence, 4, 10.1038/s42256-022-00533-0 · DOI
- H Roberts, 2023, A Comparative Framework for AI Regulatory Policy
- S P Rowland, 2022, Digital health technology-specific risks for medical malpractice liability, Npj Digital Medicine, 5, 10.1038/s41746-022-00698-3 · DOI
- C Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence, 1, 10.1038/s42256-019-0048-x · DOI · Paper page
- F Santoni De Sio, 2021, Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them, Philosophy & Technology, 34, 1057, 10.1007/s13347-021-00450-x · DOI · Paper page
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Learning to Prompt in the Classroom to Understand AI Limits: A Pilot Study
Theophilou, E., Koyutürk, C., Yavari, M., Bursic, S., Donabauer, G., Telari, A., Testa, A., Boiano, R., Hernandez-Leo, D., Ruskov, M., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-47546-7_33 · Cited by 58
Allocating Responsibility in Autonomous AI Systems: A Tiered Governance Model Under EU Regulation
Papastergiou, F., Quintero, B., & Marin, V. (2026) · Social Sciences · doi:10.3390/socsci15060392 · Cited by 1
Metadata
- Authors
- Ziosi, M., Mökander, J., Novelli, C., Casolari, F., Taddeo, M., & Floridi, L.
- Affiliations
- University of Oxford (GB)
- Princeton University (US)
- Yale University (US)
- University of Bologna (IT)
- Corresponding author
- Ziosi, Marta
- ORCID
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W7000428809
- Cited by
- 12
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- European journal of law and technology (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- AI liability, liability gap, information gap, algorithmic discrimination, legal uncertainty, legal fragmentation, transparency, disclosure of evidence, high-risk AI systems, burden of proof, access to evidence, AI regulation
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