How to Assess Trustworthy AI in Practice

• URL: http://arxiv.org/abs/2206.09887v2
• Date: Tue, 28 Jun 2022 14:23:47 GMT
• Title: How to Assess Trustworthy AI in Practice
• Authors: Roberto V. Zicari, Julia Amann, Fr\'ed\'erick Bruneault, Megan Coffee, Boris D\"udder, Eleanore Hickman, Alessio Gallucci, Thomas Krendl Gilbert, Thilo Hagendorff, Irmhild van Halem, Elisabeth Hildt, Sune Holm, Georgios Kararigas, Pedro Kringen, Vince I. Madai, Emilie Wiinblad Mathez, Jesmin Jahan Tithi, Dennis Vetter, Magnus Westerlund, Renee Wurth
• Abstract summary: Z-Inspection$smallcircledR$ is a holistic process used to evaluate the trustworthiness of AI-based technologies. Uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI.
• Score: 0.22740899647050103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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