Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness. Firms that invest seriously in safety, fairness, and oversight cannot consistently prove to consumers, regulators, and shareholders that their systems go beyond the bare minimum of compliance. What is missing is a way for society to recognize or compare the difference. The result is a trust gap: a structural condition in which responsible development efforts happen inside organizations but produce no external, independently recognized and verifiable signal of t...
| Authors | Trisevgeni Papakonstantinou, Cansu Canca, Farah Nanji et al. |
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| Primary category | cs.AI |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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