BME Architectural Audit Governance Framework (BAAGF)

The BME Architectural Audit & Governance Framework (BAAGF) introduces a lifecycle-based model for evaluating and mitigating epistemic degradation in Artificial Intelligence (AI) systems. Developed by Dale Rutherford (University of Arkansas at Little Rock), BAAGF addresses the intertwined propagation of bias, misinformation, and error across intelligent architectures—treating these as systemic vectors that erode informational integrity and public trust.

The framework establishes a unified structure for architectural traceability, lifecycle accountability, and metric integration. It operationalizes five core indices—the Bias Amplification Ratio (BAR), Error Cascade Propagation Index (ECPI), Information Quality Deviation (IQD), Adaptive Harm Reduction Score (AHRS), and Prompt Traceability & Disclosure Index (PTDI)—to quantify and monitor epistemic risk throughout the AI lifecycle. These metrics converge in a Composite BME Score (CBMES), providing a measurable signal of governance health.

BAAGF harmonizes international standards including ISO/IEC 42001, the NIST AI Risk Management Framework, the EU AI Act, IEEE 7003, and UNESCO’s Recommendation on the Ethics of AI. It functions as a meta-governance layer, enabling alignment between ethical intent, architectural design, and regulatory compliance. Within the broader ALAGF ecosystem—alongside SymPrompt+ (human–machine interaction governance) and MIDCOT (information quality and drift optimization)—BAAGF acts as the architectural conscience of intelligent systems.

The work advances the emerging paradigm of cognitive governance, positioning ethical reasoning as an intrinsic property of AI architectures rather than an external constraint. It provides a conceptual and operational foundation for auditors, policymakers, and researchers seeking to design, deploy, and monitor AI systems that are not only functional but also ethically accountable.