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Current ViewAI Governance SystemsSearch: Bioinformatics
Beyond M15: Why STEM BIO-AI Started Acting More Like a Governance Report in v1.8.x
AI Governance Systems
STEM-AI:Soverign Trust Evaluator for Medical AI Artifacts

Beyond M15: Why STEM BIO-AI Started Acting More Like a Governance Report in v1.8.x

STEM BIO-AI v1.8.x moved beyond M15 integration by turning its audit output into a clearer governance report with bounded scores, traceability, and release integrity.

Control, auditability, and safe boundaries#AI#AGI#AI Alignment#AI Governance#Biomedical#Bioinformatics#Mlops#Cognitive Science#Developer Tools#AI Research#Scientific Integrity#Prompt Engineering#Software Development#Data Orchestration#Code Review#Reproducibility#Executable artifacts#Claim custody#AISafety
When Control Becomes Authority: Calibration Governance in STEM BIO-AI 1.7.x
AI Governance Systems
STEM-AI:Soverign Trust Evaluator for Medical AI Artifacts

When Control Becomes Authority: Calibration Governance in STEM BIO-AI 1.7.x

Why STEM BIO-AI treats calibration as governed policy instead of a free-form score-tuning console for bio and medical AI repository audits.

Control, auditability, and safe boundaries#Bioinformatics#Biomedical#AI#AGI#AI Alignment#AI Governance#AI Hallucination#Cognitive Science#Open Source#AI Research#AI Code#Architecture#Data Orchestration#Agent
How Auditing 10 Bio-AI Repositories Shaped STEM-AI
AI Governance Systems
STEM-AI:Soverign Trust Evaluator for Medical AI Artifacts

How Auditing 10 Bio-AI Repositories Shaped STEM-AI

After auditing 10 open-source Bio-AI repositories, we found blind spots in STEM-AI and expanded it from text-only review to code-aware trust evaluation.

Control, auditability, and safe boundaries#AI#AI Governance#AI Hallucination#Biomedical#Bioinformatics#Mlops#Data Orchestration#Architecture
From Fail-Closed Blocking to Reproducible PASS/BLOCK Separation (EXP-032B)
AI Governance Systems
RExSyn Nexus-Bio

From Fail-Closed Blocking to Reproducible PASS/BLOCK Separation (EXP-032B)

A validation study showing how EXP-032B achieved reproducible PASS/BLOCK separation across A/B/C control arms by patching false-blocking causes, improving observability, and measuring replay drift under observer-shadow conditions.

Control, auditability, and safe boundaries#AI#AI Ethics#AI Governance#Biomedical#Bioinformatics#Mlops#Scientific Integrity#AI Research#AI Code#Architecture