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

TL;DR

This paper introduces a maturity-based framework for certifying embodied AI systems by establishing structured assessment methods, quantitative scoring, and trade-off navigation, exemplified through uncertainty quantification and a UAS detection case study.

Contribution

It presents a novel certification framework for embodied AI that incorporates explicit measurement mechanisms and structured assessment processes.

Findings

Feasibility demonstrated via UAS detection case study

Uncertainty quantification as an effective measurement mechanism

Framework supports navigating multi-objective trustworthiness trade-offs

Abstract

We propose a maturity-based framework for certifying embodied AI systems through explicit measurement mechanisms. We argue that certifiable embodied AI requires structured assessment frameworks, quantitative scoring mechanisms, and methods for navigating multi-objective trade-offs inherent in trustworthiness evaluation. We demonstrate this approach using uncertainty quantification as an exemplar measurement mechanism and illustrate feasibility through an Uncrewed Aircraft System (UAS) detection case study.