How to Assess Trustworthy AI in Practice
Research output: Working paper › Preprint › Research
Standard
How to Assess Trustworthy AI in Practice. / Zicari, Roberto V.; Amann, Julia; Bruneault, Frédérick; Coffee, Megan; Düdder, Boris; Gallucci, Alessio; Gilbert, Thomas Krendl; Hagendorff, Thilo; Halem, Irmhild van; Hickman, Eleanore; Hildt, Elisabeth; Holm, Sune; Kararigas, Georgios; Kringen, Pedro; Madai, Vince I.; Mathez, Emilie Wiinblad; Tithi, Jesmin Jahan; Vetter, Dennis; Westerlund, Magnus; Wurth, Renee.
arxiv.org, 2022.Research output: Working paper › Preprint › Research
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - UNPB
T1 - How to Assess Trustworthy AI in Practice
AU - Zicari, Roberto V.
AU - Amann, Julia
AU - Bruneault, Frédérick
AU - Coffee, Megan
AU - Düdder, Boris
AU - Gallucci, Alessio
AU - Gilbert, Thomas Krendl
AU - Hagendorff, Thilo
AU - Halem, Irmhild van
AU - Hickman, Eleanore
AU - Hildt, Elisabeth
AU - Holm, Sune
AU - Kararigas, Georgios
AU - Kringen, Pedro
AU - Madai, Vince I.
AU - Mathez, Emilie Wiinblad
AU - Tithi, Jesmin Jahan
AU - Vetter, Dennis
AU - Westerlund, Magnus
AU - Wurth, Renee
N1 - On behalf of the Z-Inspection$^{\small{\circledR}}$ initiative (2022)
PY - 2022/6/20
Y1 - 2022/6/20
N2 - 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.
AB - 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.
KW - cs.CY
KW - Trustworthy
KW - Artificial Intelligence
KW - Machine Learning
KW - Society
KW - Law & Technonology
U2 - 10.48550/arXiv.2206.09887
DO - 10.48550/arXiv.2206.09887
M3 - Preprint
BT - How to Assess Trustworthy AI in Practice
PB - arxiv.org
ER -
ID: 314388253