Multi-Dataset IQ Drift & Cost-Optimized Training (MIDCOT)

The MIDCOT Framework: Multi-Dataset IQ Drift & Cost-Optimized Training presents a comprehensive model for managing dataset integrity and epistemic stability in large language model (LLM) development. Authored by Dale Rutherford (The Center for Ethical AI), MIDCOT introduces a scientifically grounded, standards-aligned approach for monitoring, mitigating, and economically optimizing information quality (IQ) throughout AI training and retraining cycles. Built upon the foundations of Statistical Process Control (SPC) and cost-benefit modeling, MIDCOT establishes a full governance lifecycle—from IQ baseline benchmarking and drift detection to intervention decision modeling and audit-ready reporting. It defines a suite of metrics and indices including the Information Quality Baseline (IQB), Drift Index (D(t)), Echo Chamber Index (ECI), and ΔC/ΔIQ optimization ratio, forming a quantitative basis for ethical, data-driven AI oversight. The framework directly aligns with ISO/IEC 42001, ISO/IEC 23053, ISO/IEC 27001/27701, and the NIST AI Risk Management Framework, bridging quality engineering with AI governance. MIDCOT integrates case-based simulations, including medical and financial sector deployments, and provides appendices with practical toolkits, risk registers, pilot templates, and compliance crosswalks. Functioning as both a research monograph and an operational guide, MIDCOT transforms dataset governance from a reactive compliance task into a proactive, adaptive discipline. It enables organizations to sustain information quality, reduce echo-chamber effects, and optimize the balance between ethical integrity and computational efficiency—laying the foundation for trustworthy, auditable, and economically sustainable AI systems.