Independent Certification Proposed to Close AI Trust Gap

Despite significant advancements in responsible AI (RAI) practices aimed at mitigating risks in high-stakes applications, a fundamental "trust gap" persists in the market. Companies investing heavily in AI safety, fairness, and oversight struggle to independently prove their systems exceed minimal compliance to consumers, regulators, and shareholders. This issue stems from a focus on internal processes (responsible AI) rather than independently verifiable outcomes (trustworthy AI). The authors identify three compounding failures: the market's inability to differentiate truly trustworthy systems, an evaluation ecosystem that targets models and outputs instead of deployed sociotechnical systems, and a measurement approach primarily focused on harm avoidance rather than demonstrating positive benefits. By reviewing existing AI governance instruments and comparing them to certification regimes in other sectors like healthcare and sustainability, the paper concludes that no current framework effectively combines a governance baseline, independently verified positive-outcome evidence, and market signaling. The proposed solution is independent, outcome-oriented certification, which would complement existing regulation and internal governance by making trustworthiness measurable, comparable, and commercially rewarding.