Accountability Arbitrage: Ethical Tensions in AI Agent Accountability Infrastructure
An account of accountability arbitrage in AI-agent governance, accepted in AI and Ethics.
AI and Ethics · Accepted 2026-09-01
Research on economic incentives, verification, AI ethics, and institutional accountability. Explore how AI changes decisions, work, and the conditions of governance.
SSRN Working Papers present developed research. Some are maintained as standalone working papers by design. Research maturity and publication status are recorded separately.
13 papers · Academic publication first · record updated 2026-09-06
An account of accountability arbitrage in AI-agent governance, accepted in AI and Ethics.
AI and Ethics · Accepted 2026-09-01
AI-governance debates often contrast public ownership of foundation-model infrastructure with private oligopoly.
"Is an LLM conscious?" is a clear and consequential question.
Why can a commercially small state exercise influence beyond its output, while a materially large state loses leverage when alliances, payment channels, or administrative reach fracture? This article develops a three-axis theory of national influence capacity: productive capacity, mobilizable surplus, and network position.
AI companion services offer continuing, personalized conversation as friendship or intimacy.
This paper develops a two-sided theory of verification asymmetry under AI substitution.
This paper develops a theory of objective information value when payoff information improves but an agent incompletely represents how current actions change future feasibility.
Model Risk Management (MRM), set out in the Federal Reserve's SR 11-7 and OCC Bulletin 2011-12, governs quantitative models in banking.
AI-assisted procurement, counterparty selection, pricing, and logistics raise a basic question for global grain supply chains: can an enterprise reconstruct who decided what, when, and on what information? This paper distinguishes product provenance from decision provenance and develops a four-layer accountability-void framework covering definition, regulation, enforcement, and precedent.
These working papers include early ideas, initial drafts, and work awaiting further development or verification. They are shared as work in progress.
This preprint develops the borrowed-safety thesis: output-centric AI controls can reduce visible hosted-platform risk while weakening the distributed human capacity needed to secure, verify, and govern future AI deployment.
Sarbanes-Oxley Sections 302 and 404 rest on assumptions that humans design, operate, and monitor internal controls; that control operators are identifiable; and that audit evidence can be reconstructed.
Financial institutions increasingly deploy AI systems built on open source components released under permissive licenses (Apache 2.0, MIT).
On February 23, 2026, Anthropic published a blog post describing Claude Code's capabilities for COBOL modernization.