A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms

Short summary

This arXiv paper critically analyzes tools and trust mark frameworks for operationalizing trustworthy AI, using OECD data to map asymmetries in ethical focus, lifecycle coverage, and stakeholder targeting. It finds heavy emphasis on fairness, transparency, and robustness but neglect of explainability, digital security, and environmental sustainability, with most tools concentrated on post-development stages. The authors recommend expanding ethical objectives, embedding ethics across the AI lifecycle, and fostering broader multi-stakeholder participation to bridge the principles-to-practice gap.

• •OECD dataset analysis reveals TAI tools over-focus on fairness/transparency while neglecting explainability, security, and sustainability
• •Most AI governance tools target post-development stages, leaving design and data collection phases under-guided
• •Authors call for lifecycle-wide ethics embedding and broader multi-stakeholder participation in AI governance

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