How to Assess Trustworthy AI in Practice

Publikation: Working paper › Preprint › Forskning

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.

Publikation: Working paper › Preprint › Forskning

Harvard

Zicari, RV, Amann, J, Bruneault, F, Coffee, M, Düdder, B, Gallucci, A, Gilbert, TK, Hagendorff, T, Halem, IV, Hickman, E, Hildt, E, Holm, S, Kararigas, G, Kringen, P, Madai, VI, Mathez, EW, Tithi, JJ, Vetter, D, Westerlund, M & Wurth, R 2022 'How to Assess Trustworthy AI in Practice' arxiv.org. https://doi.org/10.48550/arXiv.2206.09887

APA

Zicari, R. V., Amann, J., Bruneault, F., Coffee, M., Düdder, B., Gallucci, A., Gilbert, T. K., Hagendorff, T., Halem, I. V., Hickman, E., Hildt, E., Holm, S., Kararigas, G., Kringen, P., Madai, V. I., Mathez, E. W., Tithi, J. J., Vetter, D., Westerlund, M., & Wurth, R. (2022). How to Assess Trustworthy AI in Practice. arxiv.org. https://doi.org/10.48550/arXiv.2206.09887

Vancouver

Zicari RV, Amann J, Bruneault F, Coffee M, Düdder B, Gallucci A o.a. How to Assess Trustworthy AI in Practice. arxiv.org. 2022 jun. 20. https://doi.org/10.48550/arXiv.2206.09887

Author

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. / How to Assess Trustworthy AI in Practice. arxiv.org, 2022.

Bibtex

@techreport{b56ddc6cac184acfb00bab1a70c8cd66,
title = "How to Assess Trustworthy AI in Practice",
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.",
keywords = "cs.CY, Trustworthy, Artificial Intelligence, Machine Learning, Society, Law & Technonology",
author = "Zicari, {Roberto V.} and Julia Amann and Fr{\'e}d{\'e}rick Bruneault and Megan Coffee and Boris D{\"u}dder and Alessio Gallucci and Gilbert, {Thomas Krendl} and Thilo Hagendorff and Halem, {Irmhild van} and Eleanore Hickman and Elisabeth Hildt and Sune Holm and Georgios Kararigas and Pedro Kringen and Madai, {Vince I.} and Mathez, {Emilie Wiinblad} and Tithi, {Jesmin Jahan} and Dennis Vetter and Magnus Westerlund and Renee Wurth",
note = "On behalf of the Z-Inspection$^{\small{\circledR}}$ initiative (2022)",
year = "2022",
month = jun,
day = "20",
doi = "10.48550/arXiv.2206.09887",
language = "English",
publisher = "arxiv.org",
type = "WorkingPaper",
institution = "arxiv.org",

}

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