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.
Figures & tables
Figure 1. An abbreviated version of the information flow model of the Provider’s AI system for recruitment. Inputs are a Client recruitment request, captured as a requirement configuration, and a set of candidates. The output is a list of ranked and scored candidates.
Figure 2. An abbreviated version of the information flow model of the Deployer’s recruitment process, prior to AI tool integration.
Figure 3. Abbreviated information flow model showing three different combination of the Deployer’s high-volume recruitment process and the AI tool from Figure 1 (here displayed in a compressed format). Alternative a) represents a minimal integration where the tool is used for pre-screening only. Alternative b) represents a version where both video-interviews and tool scoring are used in tandem. Alternative c) represents a version where the tool replaces both pre-screening and the video-interview.
Artificial intelligence governance studies how laws, institutions, and technical controls shape AI development, deployment, accountability, and social effects.