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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 62%Top 7 Explainable AI Companies Driving Transparent And Responsible AI Adoption - SNS Insider →
- PossiblePossibly related (embedding) · 62%Microsoft at Black Hat USA 2026: Defending trust in the age of AI and supply chain attacks - Microsoft →
- PossiblePossibly related (embedding) · 62%Securing AI Infrastructure Starts With Securing Trust - SMBtech →
- PossiblePossibly related (embedding) · 59%Anthropic Thinks Its Own Success Is Key to Making AI Safe →
- PossiblePossibly related (embedding) · 59%Most nurses say AI isn’t good enough to trust with patient care, survey finds - The Washington Post →
- LinkedLinked via arxiv author · 85%Trisevgeni Papakonstantinou →
“Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI”
- LinkedLinked via arxiv author · 85%Cansu Canca →
“Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI”
- LinkedLinked via arxiv author · 85%Farah Nanji →
“Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI”