The Human Moral Archive Framework (HMAF): From Reflection to Conscience — A Data-Driven Architecture for Empirical Machine Ethics

The Human Moral Archive Framework (HMAF) Version 2.0 consolidates the full family of constructs established across Addenda I–IX into a unified, fully quantitative moral information architecture capable of empirically evaluating artificial intelligence systems. To the best of our knowledge, HMAF represents the first quantitative, distribution based approach to machine ethics—one that transforms moral behavior into measurable data structures, statistical indices, density weighted distributions, entropy profiles, drift trajectories, and emergence forecasts. No existing framework provides comparable mathematical rigor or computational coherence. HMAF V2 is built to address the growing demands of global regulatory frameworks, including the EU AI Act, the OECD AI Principles, and the NIST AI Risk Management Framework, all of which require measurable stability, traceability, transparency, and demonstration of risk aligned system behavior over time. By quantifying moral behavior through distributional mathematics and closed-loop computational processes, HMAF provides a regulatory ready moral audit system that integrates with any AI platform through the Moral Middleware Layer (MML) without exposing proprietary internals. In doing so, the framework establishes an empirical foundation for compliance, governance, and long-horizon system monitoring.