Toward a Human-Centered Reference Framework for AI Governability: The FlowFusion Approach

This paper proposes a conceptual framework for artificial intelligence governance centered on the principle that technological capability must be accompanied by responsibility, transparency, and human-centered oversight. It introduces the concept of AI Governability, defined as the capacity of AI systems to remain understandable, steerable, accountable, and correctable throughout their lifecycle. The framework argues that AI governance should evaluate not only system performance and safety, but also the ability of societies and institutions to maintain meaningful control over increasingly powerful technologies. It identifies ten foundational governance principles and introduces the U-S-A-C model — Understandability, Steerability, Accountability, Correctability — as an analytical bridge between AI capability and institutional oversight capacity. This document does not claim to resolve all artificial intelligence governance challenges. It presents a conceptual foundation intended to support scientific discussion, critical evaluation, and further development toward responsible and sustainable AI ecosystems. It is offered as a conceptual framework requiring operational definition, measurement, and independent empirical validation — not as an established scientific theory. This is the short version of the paper. Version 1.0 — July 2026.