Trustworthy artificial intelligence: A decision-making taxonomy of potential challenges
Akbar, Muhammad Azeem; Khan, Arif Ali; Mahmood, Sajjad; Rafi, Saima; Demi, Selina (2024-05-31)
John Wiley & Sons
31.05.2024
Akbar MA, Khan AA, Mahmood S, Rafi S, Demi S. Trustworthy artificial intelligence: A decision-making taxonomy of potential challenges. Softw: Pract Exper. 2024; 54(9): 1621-1650. doi: 10.1002/spe.3216
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2023 The Authors. Software: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2023 The Authors. Software: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406244873
https://urn.fi/URN:NBN:fi:oulu-202406244873
Tiivistelmä
Abstract
The significance of artificial intelligence (AI) trustworthiness lies in its potential impacts on society. AI revolutionizes various industries and improves social life, but it also brings ethical harm. However, the challenging factors of AI trustworthiness are still being debated. This research explores the challenging factors and their priorities to be considered in the software process improvement (SPI) manifesto for developing a trustworthy AI system. The multivocal literature review (MLR) and questionnaire-based survey approaches are used to identify the challenging factors from state-of-the-art literature and industry. Prioritization based taxonomy of the challenges is developed, which reveals that lack of responsible and accountable ethical AI leaders, lack of ethics audits, moral deskilling & debility, lack of inclusivity in AI multistakeholder governance, and lack of scale training programs to sensitize the workforce on ethical issues are the top-ranked challenging factors to be considered in SPI manifesto. This study's findings suggest revising AI-based development techniques and strategies, particularly focusing on trustworthiness. In addition, the results of this study encourage further research to support the development and quality assessment of ethics-aware AI systems.
The significance of artificial intelligence (AI) trustworthiness lies in its potential impacts on society. AI revolutionizes various industries and improves social life, but it also brings ethical harm. However, the challenging factors of AI trustworthiness are still being debated. This research explores the challenging factors and their priorities to be considered in the software process improvement (SPI) manifesto for developing a trustworthy AI system. The multivocal literature review (MLR) and questionnaire-based survey approaches are used to identify the challenging factors from state-of-the-art literature and industry. Prioritization based taxonomy of the challenges is developed, which reveals that lack of responsible and accountable ethical AI leaders, lack of ethics audits, moral deskilling & debility, lack of inclusivity in AI multistakeholder governance, and lack of scale training programs to sensitize the workforce on ethical issues are the top-ranked challenging factors to be considered in SPI manifesto. This study's findings suggest revising AI-based development techniques and strategies, particularly focusing on trustworthiness. In addition, the results of this study encourage further research to support the development and quality assessment of ethics-aware AI systems.
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