Transparent Information Flow Modeling for Role-Aware AI Act Assessment

Abstract:The European Union's Artificial Intelligence Act introduces a structured framework for governing high-risk AI systems, emphasizing transparency, risk management, and role-specific accountability. However, current assessment practices often rely on monolithic system views or disconnected checklists, limiting their ability to capture the socio-technical complexity of real-world deployments. We explore the use of the Information Flow Methodology (IFM), a modeling-based approach to AI Act compliance assessment. IFM represents decision systems -- whether algorithmic, human, or hybrid -- as networks of information sites and transformation channels, enabling structured analysis of how information, decisions, and responsibilities flow across organizational and technical boundaries. We apply IFM in a real-world case involving a recruitment support tool used by a public-sector organization, evaluating both the Provider's system and the Deployer's recruitment process. Our results demonstrate that IFM can serve as a scaffold to bridge both component-based and holistic perspectives, and how technical systems are situated within socio-technical contexts. In doing so, IFM supports transparency, identifies risk pathways, and enables alignment analysis between intended and actual use. We argue that such modeling provides a foundational tool for effective, context-sensitive AI governance and accountability -- offering capabilities beyond conventional checklist-based assessments.

19 pages, 3 figures. Presented at the CLEAR Workshop, ECAI 2025

Artificial Intelligence (cs.AI)

K.4.1; K.4.3; H.4.2

arXiv:2610.16916 [cs.AI]

(or arXiv:2610.16916v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.16916 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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