ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Estimate intervention value before handoff to reduce unsafe approvals and unnecessary escalations
Escalation is triggered when estimated causal benefit exceeds review cost, not by confidence alone.
From correlation-heavy dashboards to intervention-level attribution in meta-insight governance systems
Causal OLR decomposition attributes observed learning-rate gains to specific interventions, improving budget and policy allocation decisions.
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.
How organizational knowledge graphs enable responsibility chain tracing and risk concentration detection
Standard RAG often retrieves flat document chunks that under-represent relational structure needed for causal and responsibility reasoning. Graph RAG models documents and entities as nodes in an adjacency matrix, enabling multi-hop retrieval along causal paths in organizational knowledge. We formalize an h-hop diffusion score, derive hop-depth choices from a noise-accuracy tradeoff, and introduce an evidence-cohesion metric that gates response generation by subgraph density. In contract-corpus evaluations, the method reported 73.4% causal-path extraction accuracy at 3 hops, a 31% improvement over flat Top-k RAG for responsibility-chain identification, and `r = 0.87` correlation between cohesion score and response correctness.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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