How to Assess Trustworthy AI in Practice
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866910311643086848 |
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| author | Zicari, Roberto V. Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Hickman, Eleanore Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Mathez, Emilie Wiinblad Tithi, Jesmin Jahan Vetter, Dennis Westerlund, Magnus Wurth, Renee |
| author_facet | Zicari, Roberto V. Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Hickman, Eleanore Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Mathez, Emilie Wiinblad Tithi, Jesmin Jahan Vetter, Dennis Westerlund, Magnus Wurth, Renee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_09887 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | How to Assess Trustworthy AI in Practice Zicari, Roberto V. Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Hickman, Eleanore Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Mathez, Emilie Wiinblad Tithi, Jesmin Jahan Vetter, Dennis Westerlund, Magnus Wurth, Renee Computers and Society 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. |
| title | How to Assess Trustworthy AI in Practice |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2206.09887 |