The Chancellor Trap: Administrative Mediation and the Hollowing of Sovereignty in the Algorithmic Age
- URL: http://arxiv.org/abs/2602.18474v1
- Date: Mon, 09 Feb 2026 07:28:44 GMT
- Title: The Chancellor Trap: Administrative Mediation and the Hollowing of Sovereignty in the Algorithmic Age
- Authors: Xuechen Niu,
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The contemporary governance discourse on Artificial Intelligence often emphasizes catastrophic loss-of-control scenarios. This article suggests that such framing may obscure a more immediate failure mode: chancellorization, or the gradual hollowing out of sovereignty through administrative mediation. In high-throughput, digitally legible organizations, AI-mediated decision support can reduce the probability that failures become publicly legible and politically contestable, even when underlying operational risk does not decline. Drawing on the institutional history of Imperial China, the article formalizes this dynamic as a principal-agent problem characterized by a verification gap, in which formal authority (auctoritas) remains downstream while effective governing capacity (potestas) migrates to intermediary layers that control information routing, drafting defaults, and evaluative signals. Empirical support is provided through a multi-method design combining historical process tracing with a cross-national panel plausibility probe (2016-2024). Using incident-based measures of publicly recorded AI failures and administrative digitization indicators, the analysis finds that higher state capacity and digitalization are systematically associated with lower public visibility of AI failures, holding AI ecosystem expansion constant. 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. Preserving meaningful human sovereignty therefore depends on institutional designs that deliberately reintroduce auditable friction.
Related papers
- Administrative Law's Fourth Settlement: AI and the Capability-Accountability Trap [0.0]
Since 1887, administrative law has navigated a "capability-accountability trap"<n>This Article proposes three doctrinal innovations within administrative law to realize this potential.
arXiv Detail & Related papers (2026-02-10T11:36:01Z) - Agentic Uncertainty Quantification [76.94013626702183]
We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
arXiv Detail & Related papers (2026-01-22T07:16:26Z) - Managing Ambiguity: A Proof of Concept of Human-AI Symbiotic Sense-making based on Quantum-Inspired Cognitive Mechanism of Rogue Variable Detection [39.146761527401424]
The study contributes to management theory by reframing ambiguity as a first-class construct.<n>It demonstrates the practical value of human-AI symbiosis for organizational resilience in VUCA environments.
arXiv Detail & Related papers (2025-12-17T11:23:18Z) - AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
arXiv Detail & Related papers (2025-11-10T22:24:21Z) - 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) - 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) - A new interpretable unsupervised anomaly detection method based on
residual explanation [47.187609203210705]
We present RXP, a new interpretability method to deal with the limitations for AE-based AD in large-scale systems.
It stands out for its implementation simplicity, low computational cost and deterministic behavior.
In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP.
arXiv Detail & Related papers (2021-03-14T15:35:45Z)
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.