High-Risk AI Systems and the Problem of Identity in the European AI Act

• URL: http://arxiv.org/abs/2605.23922v1
• Date: Fri, 17 Apr 2026 20:24:29 GMT
• Title: High-Risk AI Systems and the Problem of Identity in the European AI Act
• Abstract summary: The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems.<n>We show how this logic is clarified by the function+ framework of artifact identity.<n>We make the synchronic case operationally via a minimal decision flow for audit and dispute contexts.
• Score: 0.3384279376065155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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