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How to Assess Trustworthy AI in Practice

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arxiv 2206.09887 v2 pith:LRLWPLIH submitted 2022-06-20 cs.CY

classification cs.CY
keywords trustworthycircledrevaluateguidelineshlegpracticereportshare
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance

    cs.CY 2026-07 conditional novelty 5.0 of 10

    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.

  2. Getting Ready for the EU AI Act in Healthcare. A call for Sustainable AI Development and Deployment

    cs.CY 2025-05 unverdicted novelty 2.0 of 10

    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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