High-Risk AI Systems and the Problem of Identity in the European AI Act
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Abstract (arXiv)
The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems built around ex-ante conformity assessment, post-market monitoring, and re-assessment upon "substantial modification." These obligations presuppose AI identity judgments: regulators and providers must decide when an updated system remains the same system over time. In this work, we show how this logic is clarified by the function+ framework of artifact identity, which individuates AI systems by their intended function together with context-sensitive criteria of appropriate functioning, captured as "AI trustworthiness." We further argue that the AIA does not provide an internal, auditable criterion for synchronic identity--when two AI systems at a given time should count as the same for regulatory purposes--and instead largely defers such sameness determinations to sectoral or harmonization instruments. function+ supplies a synchronic identity test anchored in intended function and trustworthiness profiles and levels, making synchronic identity decisions inspectable in governance settings such as procurement, liability, and market surveillance. Our contribution is a conceptual and auditing lens: we provide a correspondence map between AIA lifecycle obligations and function+ identity components, and we make the synchronic case operationally legible via a minimal decision flow for audit and dispute contexts. We conclude with two implementation-facing recommendations: (1) more precise, testable reporting of intended purpose, and (2) standardized, auditable trustworthiness reporting that supports comparability over time and across deployments.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 17 Apr 2026.
Builds on
12 of 40 references.
References · 12
- 2023Role-Play with Large Language Modelscited by 290
- 2021Trustworthy AI: From Principles to Practicescited by 206
- 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
- 2023Auditing large language models: a three-layered approachcited by 135
- 2026Towards a Science of AI Agent Reliabilitycited by 58
- 2021Ethics-Based Auditing to Develop Trustworthy AIcited by 23
- 2021Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulationcited by 19
- 2025Robustness and Cybersecurity in the EU Artificial Intelligence Actcited by 10
- 2025A Trustworthiness-based Metaphysics of Artificial Intelligence Systemscited by 6
- 2024A Blueprint for Auditing Generative AIcited by 4
- 2023The E.U.'s Artificial Intelligence Act: An Ordoliberal Assessmentcited by 2
- 2026A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agentscited by 2
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026A Category Theory Account of AI Identitycited by 2
- 2026Update Opacity: Epistemic Accessibility and Governance Under AI System Changecited by 1
- 2026A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governancecited by 1
- 2026Operational Identity: A Finite Audit of Declared and Implemented Rules of Samenesscited by 0
- 2026Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness22 Jul 2026
- 2026A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance17 Jul 2026
- 2026A Category Theory Account of AI Identity30 Jun 2026
- 2026Update Opacity: Epistemic Accessibility and Governance Under AI System Change27 Apr 2026