Transparent Information Flow Modeling for Role-Aware AI Act Assessment

Key points

• 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.1
• 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.1
• 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.1
• We argue that such modeling provides a foundational tool for effective, context-sensitive AI governance and accountability -- offering capabilities beyond conventional checklist-based assessments.1

Sources (1)

• [1]Transparent Information Flow Modeling for Role-Aware AI Act Assessment
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:59 PM

  We explore the use of the Information Flow Methodology (IFM), a modeling-based approach to AI Act compliance assessment.

  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.

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