TAG ARCHIVE
responsibility-gate
2 MARIA OS blog articles tagged responsibility-gate. Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents. This canonical topic archive supports search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
What Deploying a Municipal AI Phone System Taught Us About the Conditions for Automating Main Switchboard Operations
Switchboard AI succeeds or fails not on speech recognition, but on the design of inquiry classification, responsibility boundaries, human-transfer conditions, and the improvement loop
When municipalities and public-interest organizations apply AI to their main switchboard lines, success is determined not by natural conversation but by a design that correctly separates which inquiries belong to whom. This article frames the AI phone system not as an FAQ, but as an operational harness.
Pricing Responsibility in Retail AI: Welfare-Constrained Dynamic Pricing with Fail-Closed Gates
A formal framework for ensuring AI-driven pricing decisions preserve consumer welfare through responsibility gates and counterfactual fairness constraints
Dynamic pricing algorithms optimize revenue in real time, but unconstrained optimization can increase vulnerability and unfair outcomes. This paper introduces a Pricing Responsibility Gate that evaluates each price change against welfare constraints, fairness criteria, and reversibility conditions, so AI pricing can remain within explicit governance boundaries while preserving business value.