OntoMotoOS Meta-Resonance Score (OMRS): A Novel Evaluation Framework for Measuring ‘Good AI’

This paper introduces the OntoMotoOS Meta-Resonance Score (OMRS), a novel framework for evaluating what constitutes “Good AI.” Existing approaches in AI evaluation focus predominantly on technical performance—such as accuracy, efficiency, or robustness—while often neglecting broader ethical and societal dimensions. OMRS addresses this gap by integrating multiple layers of assessment: foundational quality (accuracy, safety), ethical trustworthiness (fairness, privacy, transparency, accountability), and societal impact (beneficence, respect for human autonomy, inclusivity, and sustainability). Grounded in system theory and normative philosophy, the framework incorporates dynamic mechanisms such as the Identity–Autonomy–Mirroring–Feedback loop, the Themis Filter, and the PhoenixLoop, which enable continuous ethical alignment and resilience. By unifying diverse criteria into a single meta-score, OMRS offers both researchers and policymakers a practical tool for measuring, monitoring, and governing AI systems. This work thus positions OMRS as a bridge between technical benchmarks and philosophical principles, contributing to the ongoing discourse on responsible and trustworthy artificial intelligence.