TU/TUE/CIGL: Towards Epistemic Integrity, Accountability, and Failure & Risk Visibility in AI
Creators: name: Ivliev, Ihor
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Transparence Unit (TU)
subject: Transparence Unit Ecosystem (TUE)
subject: Cognitive Integrity Glue Layer (CIGL)
subject: Epistemic Integrity
subject: Epistemic Governance
subject: Epistemic Maturity
subject: Epistemic Risk Visibility
subject: Failure Visibility
subject: Epistemic Scaffolding
subject: Scaffolding Governance
subject: AI Accountability
subject: AI Auditing
subject: AI Traceability
subject: AI Justification
subject: Justification Validation
subject: Operationalized Humility
subject: Bounded Regress
subject: Incremental Assimilation
subject: G-Layers
subject: Formal Causal Justification Framework (FCJF)
subject: AI Safety
subject: Trustworthy AI
subject: AI Ethics
subject: High-Stakes AI
subject: Conceptual Architecture
subject: Cognitive Architecture
subject: Artificial intelligence
subject: Artificial Intelligence
subject: Artificial Intelligence/standards
subject: Artificial Intelligence/ethics
subject: Artificial Intelligence/legislation &amp; jurisprudence
subject: Artificial Intelligence
subject: Deep learning
subject: Deep Learning
subject: Deep Learning/ethics
subject: Deep Learning/legislation &amp; jurisprudence
subject: Deep Learning
subject: Large Language Models (LLMs)
subject: Explainable AI (XAI)
subject: AI Regulation
subject: Cross-Paradigm Governance
subject: Computer Engineering
subject: Ethics
subject: Ethics
subject: Ethics
subject: Metadata/ethics
subject: AI and Law
subject: Machine learning
subject: Machine Learning
subject: Machine Learning/ethics
subject: Machine Learning/legislation &amp; jurisprudence
subject: Epistemology
subject: Risk
subject: Risk
subject: Risk Management
subject: Risk management
subject: Residual risk
subject: Risk analysis
subject: Risk reduction
subject: Risk Assessment

description: Dominant AI paradigms, particularly connectionist models (DL, LLMs), often exhibit significant epistemic deficits due to inherent opacity and lack of intrinsic epistemic scaffolding, hindering verifiable understanding and control. This paper introduces the Transparence Unit (TU) Ecosystem (TUE), supported by the Cognitive Integrity Glue Layer (CIGL) infrastructure, as a conceptual architecture promoting intrinsic epistemic governance and risk visibility in AI.

The Transparence Unit (TU), evolving from EpistemicState, is proposed as a universal, auditable "cognitive packet" designed for embedded traceability, explicit dependency tracking (including scaffolding references), and mandatory justification relative to versioned external resources. TUs function within the TUE, managed by the CIGL runtime and governed by specialized agents (FCJF, G-Layers, AIRE, ELIM, TRACE).

The framework addresses epistemic weaknesses via mandatory justification (FCJF), staged maturity (G-Layers G7-G0), governed lifecycles, and integration pathways for opaque models requiring external validation via "epistemic scaffolding". The vision aims for systemic governance where epistemic risk profiles and justification pathways become operational properties. A phased roadmap (Proto -> Ω -> ∞) outlines development from passive logging towards operational and pinnacle governance.

This conceptual architecture requires further research and validation but aims to provide a foundation for AI systems demanding verifiable integrity, accountability, and justifiable reasoning.