The Two Boundaries: Why Behavioral AI Governance Fails Structurally

• URL: http://arxiv.org/abs/2604.27292v1
• Date: Thu, 30 Apr 2026 01:12:32 GMT
• Title: The Two Boundaries: Why Behavioral AI Governance Fails Structurally
• Abstract summary: We focus on the governance of effects, actions that AI systems perform in the world.<n>Rice's theorem proves the gap is undecidable in the general case for Turing-complete architecture.<n>We propose coterminous governance as the testable criterion for any AI governance system.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed AI systems, these boundaries are defined independently, creating three regions: governed capabilities (the only useful region), ungoverned capabilities (risk), and governance policies that address non-existent capabilities (theater). Two of the three regions are failure modes. We focus on the governance of effects: actions that AI systems perform in the world (API calls, database writes, tool invocations). This is distinct from the governance of model outputs (content quality, bias, fairness), which operates at a different level and requires different mechanisms. We present a formal framework for analyzing this structural gap. Rice's theorem (1953) proves the gap is undecidable in the general case for any Turing-complete architecture that attempts to govern effects behaviorally: no algorithm can decide non-trivial semantic properties of arbitrary programs, including the property "this program's effects comply with the governance policy." We define coterminous governance: a system property where the expressivenessboundary equals the governance boundary. We show that coterminous governance requires an architectural decision (separatingcomputation from effect) rather than a governance layer added after the fact. We show that structural governance under this separation subsumes separate governance infrastructure: governance checks become part of the execution pipeline rather than a second system running alongside it. We propose coterminous governance as the testable criterion for any AI governance system: either the two boundaries are provably identical, or risk and theater are structurally inevitable. Proofs are mechanized in Coq (454 theorems, 36 modules, 0 admitted).

Related papers

• Governing Agentic AI in FinTech [3.731168012111834]
  Financial institutions are delegating consequential decisions to agentic AI systems.<n>We argue the binding governance constraint is not capability but verifiability.<n>We develop a multilevel governance theory for agentic AI.
  arXiv  Detail & Related papers  (2026-08-11T18:52:09Z)
• Open Veins of Algorithmic Auditing: Why AI Assessment Lags Behind Its Deployment in the Global South [45.88028371034407]
  Artificial intelligence is being deployed across the Global South at a pace matching or exceeding the Global North.<n>Yet AI governance has not kept pace, and the gap is far wider in the South.<n>This paper documents patterns in the sparse field of Global South AI evaluation and explains why it so rarely occurs.
  arXiv  Detail & Related papers  (2026-07-23T13:39:38Z)
• Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol [1.5755923640031846]
  We analyze DeAI as a six-layer decentralizing stack.<n>We argue for a shift in the locus of governance from policy to protocol.
  arXiv  Detail & Related papers  (2026-05-23T12:09:56Z)
• A governance horizon for ethical-use constraints in open-weight AI models [29.29847173198524]
  Ethical constraints on open-weight AI models are a reflection of societal concerns and a foundation for AI governance policy.<n>We audit 2,142,823 model repositories on Hugging Face Hub to test whether this disclosure-based governance infrastructure can sustain traceability across deep model lineages.
  arXiv  Detail & Related papers  (2026-05-23T03:47:04Z)
• The Khipu Problem: Institutional Legibility Under Distributed Cognition [0.0]
  This paper introduces the khipu problem for distributed AI: the record can survive while the reading practice needed to interpret it decays.<n>Logs, traces, model versions, tool calls, outputs, and approval artifacts may remain available while the institutional capacity to read them as parts of one coherent cognitive episode disappears.<n>We argue that this failure is better understood as loss of interpretive continuity than as ordinary lack of observability.
  arXiv  Detail & Related papers  (2026-05-06T16:51:52Z)
• 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)
• Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries [0.0]
  We present a machine-checked formalization of structurally governed AI workflow architectures.<n>We prove that effect-level governance can be imposed without reducing internal computational expressivity.
  arXiv  Detail & Related papers  (2026-05-01T18:52:47Z)
• When Agents Evolve, Institutions Follow [52.359340707289114]
  We argue that multi-agent systems built on large language models face the same challenge.<n>Their central problem is not only individual intelligence, but collective organization.<n>We translate seven historical political institutions, spanning four canonical governance patterns, into executable multi-agent architectures.
  arXiv  Detail & Related papers  (2026-04-30T10:30:58Z)
• Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures [28.163187835238478]
  In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register.<n>We argue that such registers are not neutral mirrors of government activity, but active instruments of ontological design that configure the boundaries of accountability.
  arXiv  Detail & Related papers  (2026-04-16T20:48:35Z)
• Governance by Design: A Parsonian Institutional Architecture for Internet-Wide Agent Societies [0.2900810893770133]
  We argue that governing internet-wide agent societies requires institutional design, not merely risk enumeration or process compliance.<n>Applying Talcott Parsons' AGIL framework, we derive a prescriptive sixteen-cell institutional architecture for internet-wide agent governance.<n>We conclude with a prioritized roadmap for the missing governance infrastructure.
  arXiv  Detail & Related papers  (2026-04-13T11:37:46Z)
• Cryptographic Runtime Governance for Autonomous AI Systems: The Aegis Architecture for Verifiable Policy Enforcement [0.0]
  This paper presents Aegis, a runtime governance architecture for autonomous AI systems.<n>Aegis treats policy and legal constraints as execution conditions rather than advisory principles.
  arXiv  Detail & Related papers  (2026-03-15T04:04:57Z)
• Towards Selection as Power: Bounding Decision Authority in Autonomous Agents [0.0]
  We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty.<n>We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing.<n>Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
  arXiv  Detail & Related papers  (2026-02-16T10:10:47Z)
• Algorithmic Governance in the United States: A Multi-Level Case Analysis of AI Deployment Across Federal, State, and Municipal Authorities [0.0]
  This study examines how AI is used across federal, state, and municipal levels in the United States.<n>At the federal level, AI is most often institutionalized as a tool for high-stakes control.<n>State governments occupy a more ambiguous middle ground, where AI frequently combines supportive functions with algorithmic gatekeeping.
  arXiv  Detail & Related papers  (2026-02-09T14:36:32Z)
• Transitive Expert Error and Routing Problems in Complex AI Systems [0.0]
  We term this Transitive Expert Error (TEE), distinct from Dunning-Kruger effects, requiring expertise as precondition.<n>Two core mechanisms operate: structural similarity bias causes experts to overweight surface features while missing causal architecture differences.<n>We propose interventions: multi-expert activation with disagreement detection (router level), boundary-aware calibration (specialist level), and coverage gap detection.
  arXiv  Detail & Related papers  (2026-01-07T21:53:06Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quilt [44.522343543870804]
  We are worried that such systems, which overlook enduring moral diversity, will spark resistance, erode trust, and destabilize our institutions.<n>This paper traces the underlying problem to an often-unstated Axiom of Rational Convergence: the idea that under ideal conditions, rational agents will converge in the limit of conversation on a single ethics.<n>Treating that premise as both optional and doubtful, we propose what we call the appropriateness framework: an alternative approach grounded in conflict theory, cultural evolution, multi-agent systems, and institutional economics.
  arXiv  Detail & Related papers  (2025-05-08T12:55:07Z)
• 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)

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.