TAG ARCHIVE
responsibility
3 MARIA OS blog articles tagged responsibility. 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.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Action Router × Gate Engine Composition: Formal Theory of Responsibility-Aware Routing
How action routing and gate control compose into a provably safe routing system where each routed action carries complete responsibility provenance
Enterprise AI systems face a core tension: routers must maximize throughput and decision quality, while gate engines must enforce safety constraints and responsibility boundaries. When these subsystems are implemented independently and stacked in sequence, interface failures emerge: routed actions can satisfy routing criteria but violate gate invariants, and gate rules can block optimal routes without considering alternatives. This paper presents a formal composition theory that unifies Gate operator G and Router operator R into a composite operator G ∘ R that preserves safety invariants by construction. We prove a Safety Preservation Theorem showing the composed system maintains gate invariants while maximizing routing quality inside the feasible safety envelope. Using Lagrangian optimization, we derive the constrained-optimal routing policy and show a 31.4% routing-quality improvement over sequential stacking, with zero safety violations across 18 production MARIA OS deployments (1,247 agents, 180 days).
Decision Intelligence Theory: A Unified Framework for Responsible AI Governance
Five axioms, four pillar equations, and five theorems that transform organizational judgment into executable decision systems
Decision Intelligence Theory formalizes decision-making as a control system, integrating evidence, conflict, responsibility, execution, and learning. This capstone article presents a unified mathematical framework — five axioms, four pillar equations, and five theorems — together with implementation mappings and internal cohort analyses across finance, healthcare, legal, and manufacturing.
Quantifying Responsibility Transfer: Does Automation Actually Reduce Responsibility?
A formal model showing why AI adoption can create an illusion of reduced responsibility while outcome responsibility remains conserved
When organizations automate decisions, responsibility is often perceived as reduced. This paper separates execution responsibility from outcome responsibility, defines a formal transfer quantity `T(h->a)`, and derives a conservation result showing that total outcome responsibility stays in the human domain even as execution is automated.