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The assessment list for trustworthy artificial intelligence: A review and recommendations

In July 2020, the European Commission's High-Level Expert Group on AI (HLEG-AI) published the Assessment List for Trustworthy Artificial Intelligence (ALTAI) tool, enabling organizations to perform self-assessments of the fit of their AI systems and surrounding governance to the “7 Principles f...

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Autores principales: Radclyffe, Charles, Ribeiro, Mafalda, Wortham, Robert H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034015/
https://www.ncbi.nlm.nih.gov/pubmed/36967834
http://dx.doi.org/10.3389/frai.2023.1020592
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author Radclyffe, Charles
Ribeiro, Mafalda
Wortham, Robert H.
author_facet Radclyffe, Charles
Ribeiro, Mafalda
Wortham, Robert H.
author_sort Radclyffe, Charles
collection PubMed
description In July 2020, the European Commission's High-Level Expert Group on AI (HLEG-AI) published the Assessment List for Trustworthy Artificial Intelligence (ALTAI) tool, enabling organizations to perform self-assessments of the fit of their AI systems and surrounding governance to the “7 Principles for Trustworthy AI.” Prior research on ALTAI has focused primarily on specific application areas, but there has yet to be a comprehensive analysis and broader recommendations aimed at proto-regulators and industry practitioners. This paper therefore starts with an overview of this tool, including an assessment of its strengths and limitations. The authors then consider the success by which the ALTAI tool is likely to be of utility to industry in improving understanding of the risks inherent in AI systems and best practices to mitigate such risks. It is highlighted how research and practices from fields such as Environmental Sustainability, Social Justice, and Corporate Governance (ESG) can be of benefit for addressing similar challenges in ethical AI development and deployment. Also explored is the extent to which the tool is likely to be successful in being taken up by industry, considering various factors pertaining to its likely adoption. Finally, the authors also propose recommendations applicable internationally to similar bodies to the HLEG-AI regarding the gaps needing to be addressed between high-level principles and practical support for those on the front-line developing or commercializing AI tools. In all, this work provides a comprehensive analysis of the ALTAI tool, as well as recommendations to relevant stakeholders, with the broader aim of promoting more widespread adoption of such a tool in industry.
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spelling pubmed-100340152023-03-24 The assessment list for trustworthy artificial intelligence: A review and recommendations Radclyffe, Charles Ribeiro, Mafalda Wortham, Robert H. Front Artif Intell Artificial Intelligence In July 2020, the European Commission's High-Level Expert Group on AI (HLEG-AI) published the Assessment List for Trustworthy Artificial Intelligence (ALTAI) tool, enabling organizations to perform self-assessments of the fit of their AI systems and surrounding governance to the “7 Principles for Trustworthy AI.” Prior research on ALTAI has focused primarily on specific application areas, but there has yet to be a comprehensive analysis and broader recommendations aimed at proto-regulators and industry practitioners. This paper therefore starts with an overview of this tool, including an assessment of its strengths and limitations. The authors then consider the success by which the ALTAI tool is likely to be of utility to industry in improving understanding of the risks inherent in AI systems and best practices to mitigate such risks. It is highlighted how research and practices from fields such as Environmental Sustainability, Social Justice, and Corporate Governance (ESG) can be of benefit for addressing similar challenges in ethical AI development and deployment. Also explored is the extent to which the tool is likely to be successful in being taken up by industry, considering various factors pertaining to its likely adoption. Finally, the authors also propose recommendations applicable internationally to similar bodies to the HLEG-AI regarding the gaps needing to be addressed between high-level principles and practical support for those on the front-line developing or commercializing AI tools. In all, this work provides a comprehensive analysis of the ALTAI tool, as well as recommendations to relevant stakeholders, with the broader aim of promoting more widespread adoption of such a tool in industry. Frontiers Media S.A. 2023-03-09 /pmc/articles/PMC10034015/ /pubmed/36967834 http://dx.doi.org/10.3389/frai.2023.1020592 Text en Copyright © 2023 Radclyffe, Ribeiro and Wortham. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Radclyffe, Charles
Ribeiro, Mafalda
Wortham, Robert H.
The assessment list for trustworthy artificial intelligence: A review and recommendations
title The assessment list for trustworthy artificial intelligence: A review and recommendations
title_full The assessment list for trustworthy artificial intelligence: A review and recommendations
title_fullStr The assessment list for trustworthy artificial intelligence: A review and recommendations
title_full_unstemmed The assessment list for trustworthy artificial intelligence: A review and recommendations
title_short The assessment list for trustworthy artificial intelligence: A review and recommendations
title_sort assessment list for trustworthy artificial intelligence: a review and recommendations
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034015/
https://www.ncbi.nlm.nih.gov/pubmed/36967834
http://dx.doi.org/10.3389/frai.2023.1020592
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