Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

• URL: http://arxiv.org/abs/2603.20248v1
• Date: Tue, 10 Mar 2026 12:24:30 GMT
• Title: Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions
• Abstract summary: This paper proposes a rigorous coupled dynamics model that integrates a discrete-time Hawkes process.<n>A key innovation is the bidirectional feedback mechanism: declining trust amplifies the intensity of subsequent controversy events.<n> Numerical experiments reveal how echo chamber network structures and media amplification accelerate governance failure.
• Score: 0.49157446832511503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As artificial intelligence (AI) is increasingly deployed in high-stakes public decision-making (from resource allocation to welfare distribution), public trust in these systems has become a critical determinant of their legitimacy and sustainability. Yet existing AI governance research remains largely qualitative, lacking formal mathematical frameworks to characterize the precise conditions under which public trust collapses. This paper addresses that gap by proposing a rigorous coupled dynamics model that integrates a discrete-time Hawkes process -- capturing the self-exciting generation of AI controversy events such as perceived algorithmic unfairness or accountability failures -- with a Friedkin-Johnsen opinion dynamics model that governs the evolution of institutional trust across social networks. A key innovation is the bidirectional feedback mechanism: declining trust amplifies the intensity of subsequent controversy events, which in turn further erode trust, forming a self-reinforcing collapse loop. We derive closed-form equilibrium solutions and perform formal stability analysis, establishing the critical spectral condition rho(J_{2nt}) < 1 that delineates the boundary between trust resilience and systemic collapse. Numerical experiments further reveal how echo chamber network structures and media amplification accelerate governance failure. Our core contribution to the AI governance field is a baseline collapse model: a formal stability analysis framework demonstrating that, absent strong institutional intervention, even minor algorithmic biases can propagate through social networks to trigger irreversible trust breakdown in AI governance systems.

Related papers

• A game theory for foundation models shows new paths to rational cooperation through similarity inference [56.53805518668248]
  We show that foundation model agents engaging in optimal planning consistently converge to stable cooperation.<n>By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit.
  arXiv  Detail & Related papers  (2026-08-04T17:24:33Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution [80.98492754957466]
  Core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously.<n>This paper argues that causality is necessary to understand and balance trade-offs in performance and multiple objectives of trustworthy AI.
  arXiv  Detail & Related papers  (2026-05-04T14:26:28Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI [0.0]
  This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity.<n>We argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than calibrated constitutional values.
  arXiv  Detail & Related papers  (2026-02-20T11:58:43Z)
• The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies [57.387081435669835]
  Multi-agent systems built from large language models offer a promising paradigm for scalable collective intelligence and self-evolution.<n>We show that an agent society satisfying continuous self-evolution, complete isolation, and safety invariance is impossible.<n>We propose several solution directions to alleviate the identified safety concern.
  arXiv  Detail & Related papers  (2026-02-10T15:18:19Z)
• The Chancellor Trap: Administrative Mediation and the Hollowing of Sovereignty in the Algorithmic Age [0.0]
  In high- throughput organizations, AI-mediated decision support can reduce the probability that failures become publicly legible and politically contestable.<n>The article formalizes this dynamic as a principal-agent problem characterized by a verification gap.<n>The results are consistent with a paradox of competence: governance systems may become more effective at absorbing and resolving failures internally while simultaneously raising the threshold at which those failures become politically visible.
  arXiv  Detail & Related papers  (2026-02-09T07:28:44Z)
• The Principle of Proportional Duty: A Knowledge-Duty Framework for Ethical Equilibrium in Human and Artificial Systems [0.0]
  This paper introduces the Principle of Proportional Duty (PPD), a novel framework that models how ethical responsibility scales with an agent's epistemic state.<n>As uncertainty increases, Action Duty (the duty to act decisively) is proportionally converted into Repair Duty (the active duty to verify, inquire, and resolve uncertainty).<n>This paper applies the framework across four domains, clinical ethics, recipient-rights law, economic governance, and artificial intelligence, to demonstrate its cross-disciplinary validity.
  arXiv  Detail & Related papers  (2025-12-07T02:37:07Z)
• Benchmarking is Broken -- Don't Let AI be its Own Judge [22.93026946593552]
  We argue that the current laissez-faire approach to evaluating AI is unsustainable.<n>We introduce PeerBench, a community-governed, proctored evaluation blueprint.<n>Our goal is to pave the way for evaluations that can restore integrity and deliver genuinely trustworthy measures of AI progress.
  arXiv  Detail & Related papers  (2025-10-08T21:41:37Z)
• 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)
• Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring [0.174048653626208]
  Article argues that AI systems inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints.<n>We show how algorithmic processes transform historical inequalities, such as elite credential privileging and network homophily, into ostensibly meritocratic outcomes.<n>We propose mitigation strategies, including counterfactual fairness testing, capital-aware auditing, and regulatory interventions, to disrupt this self-reinforcing inequality.
  arXiv  Detail & Related papers  (2025-07-12T10:03:20Z)
• Human-AI Governance (HAIG): A Trust-Utility Approach [0.0]
  This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships.<n>Our analysis reveals how technical advances in self-supervision, reasoning authority, and distributed decision-making drive non-uniform trust evolution.
  arXiv  Detail & Related papers  (2025-05-03T01:57:08Z)

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.