Governance-First Machine Learning - A phase-separated control framework for maintaining epistemic integrity in large-scale AI systems

This repository contains a governance and control framework for the safe, disciplined implementation of multi-phase machine learning and reinforcement learning systems in complex, high-risk domains. The document addresses a recurring structural problem in large AI projects: while system architectures may be conceptually sound, learning phases (ML/RL) often introduce epistemic drift, uncontrolled feedback loops, goal substitution, or implicit optimization pressures that were not present in the original system design. These effects are rarely caused by malicious intent, but by insufficient separation of roles, phases, and decision authorities. The proposed framework introduces a governance-first approach to machine learning, emphasizing explicit phase separation, read-only learning stages, escalation paths, and stop criteria. Learning is treated as an analytical activity embedded within a broader system lifecycle, rather than as an implicit driver of system behavior. Key elements include: Explicit separation of system dynamics, observation, and learning Phase-specific permissions and prohibitions for ML and RL components Prevention of hidden feedback channels and reward leakage Human-in-the-loop checkpoints and escalation mechanisms PDCA-style control loops applied to epistemic integrity rather than performance optimization Criteria for freeze, rollback, and phase transition decisions The framework is illustrated using the AI.LC-Analyzer as a reference implementation. In this context, the document specifies how Phase 2 (read-only machine learning) and Phase 3 (conceptually constrained reinforcement learning) can be conducted without modifying or destabilizing the underlying system geometry established in Phase 1. However, the governance principles themselves are not domain-specific and are intended to be transferable to other large-scale AI systems in medicine, engineering, and socio-technical contexts. This document does not define algorithms, models, or performance benchmarks. Its contribution lies in the operationalization of epistemic discipline: making explicit what learning systems are allowed to do, what they are not allowed to do, and how deviations are detected and managed. The document is intended for system architects, machine learning engineers, researchers, and governance stakeholders who are responsible for maintaining system integrity across extended AI project lifecycles. Disclosure: This document is published as a public prior-art disclosure. It provides a methodological and governance framework for the controlled use of machine learning and reinforcement learning in complex systems. It does not prescribe clinical actions, automated decisions, or system optimizations.