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How to Assess Trustworthy AI in Practice
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abstract
This report is a methodological reflection on Z-Inspection$^{\small{\circledR}}$. Z-Inspection$^{\small{\circledR}}$ is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system.
Forward citations
Cited by 2 Pith papers
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A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
A governance-relative trustworthiness level for an AI system can be represented as a decision-tree rule learned from expert-labeled trustworthiness profiles, then monitored with boundary-margin and profile-drift diagnostics.
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Getting Ready for the EU AI Act in Healthcare. A call for Sustainable AI Development and Deployment
A position paper urging proactive, ethics-driven preparation for the EU AI Act's 2026 healthcare obligations, illustrated with the authors' Z-Inspection® assessment method.
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