Subject Roles in the EU AI Act: Mapping and Regulatory Implications

• URL: http://arxiv.org/abs/2510.13591v1
• Date: Wed, 15 Oct 2025 14:21:30 GMT
• Title: Subject Roles in the EU AI Act: Mapping and Regulatory Implications
• Abstract summary: The European Union's Artificial Intelligence Act (Regulation) 2024/1689) establishes the world's first comprehensive regulatory framework for AI systems.<n>This paper provides a structured examination of the six main categories of actors - providers, deployers, authorized representatives, importers, distributors, and product manufacturers.<n>We map the complete governance structure and analyze how the AI Act regulates these subjects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689) establishes the world's first comprehensive regulatory framework for AI systems through a sophisticated ecosystem of interconnected subjects defined in Article 3. This paper provides a structured examination of the six main categories of actors - providers, deployers, authorized representatives, importers, distributors, and product manufacturers - collectively referred to as "operators" within the regulation. Through examination of these Article 3 definitions and their elaboration across the regulation's 113 articles, 180 recitals, and 13 annexes, we map the complete governance structure and analyze how the AI Act regulates these subjects. Our analysis reveals critical transformation mechanisms whereby subjects can assume different roles under specific conditions, particularly through Article 25 provisions ensuring accountability follows control. We identify how obligations cascade through the supply chain via mandatory information flows and cooperation requirements, creating a distributed yet coordinated governance system. The findings demonstrate how the regulation balances innovation with the protection of fundamental rights through risk-based obligations that scale with the capabilities and deployment contexts of AI systems, providing essential guidance for stakeholders implementing the AI Act's requirements.

Related papers

• Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure [1.1002771741647328]
  The paper contributes a regulatory gap analysis and governance architecture for an important class of urban AI.<n>It presents a three-layer architecture (Agent, Orchestration, City) specifying 25 governance measures.<n>Five conflict resolution rules and an autonomy-calibrated activation model complete the design.
  arXiv  Detail & Related papers  (2026-05-01T20:50:34Z)
• How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications [1.0742675209112622]
  Modern AI hiring systems operate within complex supply chains where responsibility fragments across data vendors, model developers, platform providers, and deploying organizations.<n>This paper investigates how these dependency chains complicate bias evaluation and accountability attribution.<n>We propose multi-layered interventions including system-level audits, vendor guidelines, continuous monitoring mechanisms, and documentation across dependency chains.
  arXiv  Detail & Related papers  (2026-04-24T16:01:35Z)
• A pragmatic approach to regulating AI agents [0.7276372008305616]
  We argue that the unique capacity of agents to autonomously reason, plan, and execute tasks requires a shift in oversight toward the orchestration layer.<n>Our proposals highlight the need for robust accountability mechanisms to manage this heightened autonomy.<n>We advocate for a "traffic light" system of staggered task authorization based on operational risk and the creation of a statutory list of non-delegable legal acts.
  arXiv  Detail & Related papers  (2026-04-16T13:04:52Z)
• From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework [0.0]
  The study presents a cross empirical analysis of 480 realworld AI incidents from the AI Incident Database.<n>The results reveal substantial governance gaps, indicating persistent weaknesses in post-deployment accountability.<n>The study proposes the Proactive AI Governance Compliance Framework (PAGCF) to shift governance from reactive incident response toward pre-deployment compliance.
  arXiv  Detail & Related papers  (2026-04-10T21:22:55Z)
• AI Agents Under EU Law [31.882541856960888]
  We present a practical taxonomy of nine agent deployment categories mapping concrete actions to regulatory triggers.<n>We propose a twelve-step compliance architecture and a regulatory trigger mapping connecting agent actions to applicable legislation.<n>We conclude that high-risk agentic systems with untraceable behavioral drift cannot currently satisfy the AI Act's essential requirements.
  arXiv  Detail & Related papers  (2026-04-06T11:47:38Z)
• Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections [53.468443050795344]
  The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape.<n>As AI paradigms shift towards greater autonomy, specifically in the form of agentic AI, it becomes increasingly challenging to articulate regulatory stipulations.<n>This paper reviews the evolving European Union (EU) AI regulatory provisions via analyzing 24 relevant documents published between 2024 and 2025.
  arXiv  Detail & Related papers  (2026-03-19T13:50:52Z)
• A five-layer framework for AI governance: integrating regulation, standards, and certification [0.6875312133832078]
  The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation.<n>Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement.<n>A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes.
  arXiv  Detail & Related papers  (2025-09-14T16:19:08Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• The Fundamental Rights Impact Assessment (FRIA) in the AI Act: Roots, legal obligations and key elements for a model template [55.2480439325792]
  Article aims to fill existing gaps in the theoretical and methodological elaboration of the Fundamental Rights Impact Assessment (FRIA)<n>This article outlines the main building blocks of a model template for the FRIA.<n>It can serve as a blueprint for other national and international regulatory initiatives to ensure that AI is fully consistent with human rights.
  arXiv  Detail & Related papers  (2024-11-07T11:55:55Z)
• RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
  We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages.<n>We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents.<n>We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• The Artificial Intelligence Act: critical overview [0.0]
  This article provides a critical overview of the recently approved Artificial Intelligence Act. It starts by presenting the main structure, objectives, and approach of Regulation (EU) 2024/1689. The text concludes that even if the overall framework can be deemed adequate and balanced, the approach is so complex that it risks defeating its own purpose.
  arXiv  Detail & Related papers  (2024-08-30T21:38:02Z)
• Navigating the EU AI Act: A Methodological Approach to Compliance for Safety-critical Products [0.0]
  This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We then propose a contract-based approach to derive technical requirements at the stakeholder level.
  arXiv  Detail & Related papers  (2024-03-25T14:32:18Z)
• Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation [14.704747149179047]
  We argue that compute providers should have legal obligations and ethical responsibilities associated with AI development and deployment. Compute providers can play an essential role in a regulatory ecosystem via four key capacities.
  arXiv  Detail & Related papers  (2024-03-13T13:08:16Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Transparency, Compliance, And Contestability When Code Is(n't) Law [91.85674537754346]
  Both technical security mechanisms and legal processes serve as mechanisms to deal with misbehaviour according to a set of norms. While they share general similarities, there are also clear differences in how they are defined, act, and the effect they have on subjects. This paper considers the similarities and differences between both types of mechanisms as ways of dealing with misbehaviour.
  arXiv  Detail & Related papers  (2022-05-08T18:03:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.