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 acceptance first · Records updated 2026-10-11 · Preliminary drafts are grouped separately.
Developed from ethical questions raised while building AEGIS, an infrastructure for recording and verifying AI-agent actions. The paper examines how easier-to-review records can favor AI workflows over human work, and how greater scrutiny can encourage avoidance.
AI and Ethics · Accepted 2026-09-01 · Published 2026-09-14
Studies the conditions under which access, portability and verification in AI infrastructure can be analyzed separately from ownership.
Examines how inherited consciousness tests should be assessed when applied to a novel target such as a large language model, while leaving the yes-or-no question open.
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.
Studies product design, recognition and reciprocity in continuing relationships with AI companions.
Examines both sides of verification asymmetry under AI substitution, including its effects on apprenticeship and the renewal of human checking capacity.
This paper develops a theory of objective information value when payoff information improves but an agent incompletely represents how current actions change future feasibility.
Examines structural gaps between model-risk management and code transformations carried out by AI systems.
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.
Examines structural gaps in Sarbanes–Oxley internal controls when AI agents take part in financial reporting and decision processes.
Analyzes how open-source licensing and financial regulation shape accountability for deployments of AI agents.
Uses a market event to examine the gap between perceived and demonstrated AI capabilities in large-scale software modernization.
