An Initiative-Scale Structure for Reliable AI: Governance-Centrical Architecture for Reliability, Difficult, and Active Policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enterprise adoption of artificial intelligence has shifted from isolated prediction services toward deeply integrated platforms that influence workflows, customer interactions, compliance obligations, and operational resilience. This shift has created a practical challenge: organizations can no longer treat governance, system reliability, and software testing as separate disciplines. A model may be accurate in development but still fail in production because of data drift, weak controls, missing lineage, insufficient monitoring, or inadequate rollback mechanisms. This paper presents a converged architecture for trustworthy AI systems that unifies governance controls, reliability engineering, and automated testing into a single enterprise operating model. The proposed architecture is derived from prior work on trustworthy AI frameworks, lifecycle assurance, MLOps, AIOps, observability, and architecture-centered software governance. It organizes enterprise AI into five interoperable layers: policy and risk governance, data and feature integrity, model assurance, runtime observability, and continuous improvement. The paper also introduces a trust evidence loop in which policy artifacts, test outputs, telemetry, and post-deployment findings are continuously linked for auditability and operational learning. Rather than proposing trustworthiness as a static checklist, the paper treats it as a measurable systems property sustained through design-time and run-time evidence. The result is an architecture intended to improve reliability, accelerate compliant delivery, reduce hidden technical debt, and strengthen organizational confidence in AI-enabled enterprise platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it