Final Authority in AI Governance: Frontier-Provider Sovereignty and Action-Centered Deployer Governance
- URL: http://arxiv.org/abs/2607.13040v1
- Date: Fri, 12 Jun 2026 03:28:06 GMT
- Title: Final Authority in AI Governance: Frontier-Provider Sovereignty and Action-Centered Deployer Governance
- Abstract summary: This paper examines where final authority should sit once capable AI systems are embedded in organizational sovereignty.<n>The first, frontier-provider, assigns privileged authority to the provider of the most capable models.<n>The second, action-centered deployer sovereignty, places final authority over high-impact actions with the organization that authorizes the action.
- Score: 0.6526824510982799
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper examines where final authority should sit once capable AI systems are embedded in organizational workflows. It compares two governance models. The first, frontier-provider sovereignty, assigns privileged authority to the provider of the most capable models and is reflected in contemporary arguments for frontier-model testing, release gating, transparency duties, and compute-related controls. The second, action-centered deployer sovereignty, places final authority over high-impact actions with the organization that authorizes the action, embeds it in a business process, and bears the downstream legal, operational, and commercial consequences. The paper combines comparative reading of public governance frameworks with implementation-informed analysis of runtime heterogeneity and enterprise control requirements. It compares EU AI Act guidance, the NIST AI Risk Management Framework, Singapore's Model AI Governance Framework for Agentic AI, recent Japanese AI policy instruments, and Canada's voluntary code and managerial guidance. Across these materials, the paper finds stronger support for distributed operational accountability than for unilateral frontier-provider control. It further argues that rapid enterprise adoption, declining provider transparency, and widening control gaps increase the value of a portable governance layer centered on governed action rather than on provider-native session objects. The conclusion is layered rather than absolutist: strong upstream authority remains justified for frontier capability gating, but final authority over concrete enterprise action is better located with the deployer and consequence-bearer.
Related papers
- Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems [45.89982596338141]
We introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure.<n>We introduce novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG)<n>We conduct a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
arXiv Detail & Related papers (2026-06-05T19:33:58Z) - The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control [0.0]
This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance.<n>It argues that under conditions of increasing regulatory expansion, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.<n>It concludes that the central risk in AI governance may not be the absence of governance structures, but the emergence of institutions that appear increasingly governed while progressively losing the capacity to govern effectively.
arXiv Detail & Related papers (2026-05-23T17:46:19Z) - From Assistance to Agency: Rethinking Autonomy and Control in CI/CD Pipelines [0.0]
This paper presents a vision of agentic Continuous Integration and Continuous Deployment (CI/CD) in which the central challenge is not improving task performance but designing authority transfer.<n> Drawing on research prototypes and industrial platforms, we show that current systems operate mainly at the data plane under bounded autonomy.<n>We identify three recurring patterns: constrained autonomy as the dominant design, external governance as the primary safety mechanism, and a widening gap between deployment momentum and evaluation methodology.
arXiv Detail & Related papers (2026-05-08T00:15:43Z) - Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents [4.064061517379954]
The rapid deployment of autonomous AI agents across enterprise, healthcare, and safety-critical environments has created a fundamental governance gap.<n>We address this gap by drawing on how humans self-govern naturally: before acting, humans engage deliberate cognitive processes grounded in executive function.<n>This paper proposes a neurocognitive governance framework that formally maps this human self-governance process to agent reasoning.
arXiv Detail & Related papers (2026-04-28T14:15:20Z) - Preserving Decision Sovereignty in Military AI: A Trade-Secret-Safe Architectural Framework for Model Replaceability, Human Authority, and State Control [3.719121868494767]
This paper argues that the central strategic issue is not merely access to capable models, but preservation of decision sovereignty.<n>The paper develops a trade-secret-safe architectural formulation of the Energetic Paradigm as a layered, model-agnostic command-support design.
arXiv Detail & Related papers (2026-03-26T04:52:09Z) - 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) - I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems [8.670200461690454]
We present evidence that integrity in institutional AI should be treated as a pre-deployment requirement rather than a post-deployment assumption.<n>We evaluate multi-agent governance simulations in which agents occupy formal governmental roles under different authority structures.<n>We score rule-breaking and abuse outcomes with an independent-based judge across 28,112 transcript segments.
arXiv Detail & Related papers (2026-03-19T13:34:54Z) - Artificial Intelligence in Government: Why People Feel They Lose Control [44.99833362998488]
The use of Artificial Intelligence in public administration is expanding rapidly.<n>While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability.<n>This article applies principal-agent theory to AI adoption as a special case of delegation.
arXiv Detail & Related papers (2025-05-02T07:46:41Z) - AI Behind Closed Doors: a Primer on The Governance of Internal Deployment [33.99253912746621]
Internal deployment is a key source of benefits and risks from frontier AI systems.<n>This report aims to address this absence by priming a conversation around the governance of internal deployment.<n>It discusses the risks correlated to the loss of control via the internal application of a misaligned AI system to the AI research and development pipeline.
arXiv Detail & Related papers (2025-04-16T15:21:13Z) - 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 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)
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.