Theoretical Framework for Auditable AI Infrastructures

This work proposes a theoretical framework for auditable and energy-aware AI infrastructures.It models how governance, semantic value and energy efficiency can interact in self-regulating AI systems.The framework integrates three conceptual modules — the Semantic Investment Loop (SIL), the Resonance Credit Mechanism (RCM), and the Audit Energy Conversion Protocol (AECP) — forming a coherent model where audit transparency acts as an internal energy source. The content is purely theoretical and non-operational.No proprietary information is used. License: CC BY-ND 4.0Author: Winfried BruecknerKeywords: AI Governance, Auditability, Energy Efficiency, Kybernetics, Conceptual Model Allied-Safe · Civil-Use · Audit-Always. This conceptual framework aligns with regulatory baselines without duplicating them. The EU AI Act requires technical documentation for high-risk systems (Article 11 and Annex IV), which we treat as a compliance substrate rather than a dynamic control loop. EUR-Lex+2EU Künstliche Intelligenz Gesetz+2ISO/IEC 42001 defines an AI management system (AIMS) at the process/governance layer; our contribution introduces quantitative feedback couplings between audit integrity, semantic coherence, and energy efficiency. ISO+1The NIST AI RMF 1.0 (and the 2024 Generative AI Profile) provides risk/trust scaffolding (GOVERN–MAP–MEASURE–MANAGE) that we complement with mathematically specified audit-energy loops. NIST+2NIST+2Transparency artefacts such as Model Cards and Datasheets for Datasets inform our audit inputs, but they do not specify closed-loop control; here we integrate them into an explicit audit integrity index. arXiv+3arXiv+3arXiv+3On the energy side, Green AI advocates efficiency as a research objective, and Strubell et al. quantify energy/CO₂ costs for NLP; we adopt their measurement ethos while coupling it to governance signals. arXiv+3ACM Digital Library+3arXiv+3Real-world carbon-aware computing shows time/region shifting of non-urgent workloads (e.g., Google’s data centers), which our AECP formalizes as stability actions driven by audit signals. blog.google+1For measurement, CodeCarbon operationalizes CO₂ estimation as carbon-intensity × energy, providing a practical data source for our energy-efficiency ratio. mlco2.github.ioFinally, our hash-based Evidence Logger draws on Certificate Transparency (RFC 6962/9162): append-only Merkle logs and inclusion/consistency proofs—repurposed here to secure governance KPIs. rfc-editor.org+1