Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms

Researchers propose a maturity-based certification framework for embodied AI systems using explicit measurement techniques. The approach requires structured evaluation frameworks, quantitative scoring mechanisms, and methods for managing multi-objective trade-offs in reliability assessment. Using uncertainty quantification as a key measurement technique, they demonstrate feasibility through an unmanned aerial system case study, advancing trustworthy AI certification standards.