High-Risk AI Systems and the Problem of Identity in the European AI Act
For regulators and deployers of high-risk AI systems, the paper offers a conceptual lens to operationalize identity judgments required by the EU AI Act, but it is primarily a conceptual/auditing contribution without empirical validation.
The paper identifies that the EU AI Act's lifecycle governance for high-risk AI systems depends on identity judgments (when an updated system remains the same), which the Act does not adequately address. The authors propose a 'function+' framework to provide an auditable criterion for synchronic identity, enabling inspectable decisions in procurement, liability, and market surveillance.
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.