TAG ARCHIVE
ethics
4 MARIA OS blog articles tagged ethics, organized as a Bonginkan topic archive for 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.
Decision Civilization Infrastructure: From Ethics-as-Architecture to the Universal Responsibility Operating System
The capstone synthesis — why the AGI era demands not smarter AI but better responsibility structures, and how MARIA OS unifies capital, physical, ethical, and organizational decisions under a single governance topology
Every decision an organization makes — from board strategy to robot arm trajectory, from capital allocation to ethical constraint evaluation — flows through an implicit responsibility structure. In most organizations, that structure is invisible, informal, and fragile. This paper presents the Decision Civilization Infrastructure: a unified mathematical framework that formalizes the entire decision space as a product manifold D = D_capital x D_physical x D_ethical x D_organizational, proves that responsibility is a conserved quantity under decision composition, derives scaling theorems for governance preservation as systems grow, and demonstrates that all prior MARIA OS research programs — ethics formalization, ethical learning, agentic company design, investment engines, robot judgment, responsibility decomposition, gate control theory, and quality convergence — are projections of a single underlying architecture. We introduce a category-theoretic view of decision composition across domains, establish information-theoretic bounds on decision quality, and prove convergence of all subsystems toward a stable governance attractor. The competitive moat is not AI capability but structural responsibility: mathematics, reproducibility, and fail-closed architecture that compounds over time.
Manipulation Detection in Retail AI: Causal Inference for the Personalization–Manipulation Boundary
Defining the mathematical boundary between helpful personalization and harmful manipulation using causal reasoning and responsibility gates
Retail recommendation systems operate between beneficial personalization and potentially manipulative behavior. This paper introduces a causal-inference framework that defines the personalization-manipulation boundary, enabling retail AI agents to operate within explicit ethical constraints while routing boundary violations to human review.
Fairness Score Design for Insurance AI: Discrimination Detection Through Correlation Matrix Analysis
Evaluating algorithmic discrimination in insurance pricing and underwriting using correlation matrices and responsibility-gated fairness enforcement
Insurance AI systems can inherit historical bias from training data. Detecting discrimination requires more than demographic-parity checks, including analysis of indirect pathways between protected attributes and pricing features. This paper introduces a correlation-matrix-based fairness score to detect direct and proxy discrimination, paired with gate-based enforcement before decisions reach customers.
Ethics as Executable Architecture: Formalizing Moral Constraints as Computable Structures in Multi-Agent Systems
Why ethics must be structurally implemented, not merely declared, for responsible AI governance
Ethics declarations without enforcement are insufficient for production governance. This paper presents five mathematical frameworks for converting ethical principles into computable constraint structures in multi-agent systems: constraint formalization, ethical-drift detection, multi-universe conflict mapping, human-oversight calibration, and ethics-sandbox simulation before deployment. Together, these components define an Agentic Ethics Lab model for structurally implementing responsible AI.