When is AI-mediated institutional action ethically warranted? A feasibility-first framework for admissible-state reasoning

Abstract

Artificial intelligence systems increasingly mediate institutional action through forecasts, rankings, risk scores, allocation tools, automated alerts and decision-support infrastructures. These outputs can shape who receives attention, which risks are escalated, how resources are distributed and when institutions intervene, defer or withhold action. Ethical evaluation commonly addresses accuracy, fairness, transparency, explainability and human oversight. These criteria are necessary but do not by themselves establish whether acting on an AI output is ethically warranted under an institution’s current legal authority, operational capacity, temporal window, accountability structure and remedial arrangements. This paper develops a feasibility-first framework for admissible-state reasoning within Deterministic Systems Intelligence (DSI). It distinguishes continuity with earlier DSI release-governance work from a further theoretical question: whether an institution is justified in exercising consequential authority through a proposed AI-mediated action. Institutional action is represented as movement within an admissible action space shaped by governance clocks, regime configurations, structural memory, capacity ceilings, accountability gates and implementation latency. A layered governance operator separates release disposition, governance condition and boundary status while retaining the operational vocabulary RELEASE, MONITOR, ESCALATE, HOLD and BREACH. The central claim is bounded: an action should not be treated as ethically available merely because it is technically possible, statistically supported or institutionally preferred. Ethical warrant also requires legitimate authority, proportionality, feasible implementation, consideration of affected parties, contestability, monitoring and access to remedy; where an AI-mediated pathway is withheld, continuing institutional obligations may still require an alternative action. The framework therefore extends responsible AI assessment from model properties and operational release-readiness to the institutional conditions under which algorithmic outputs acquire ethically justified operational force.