Requirements practices and gaps when engineering human-centered Artificial Intelligence systems

Ahmad, K; Abdelrazek, M; Arora, C; Bano, M; Grundy, J

Requirements practices and gaps when engineering human-centered Artificial Intelligence systems

Ahmad, K Abdelrazek, M Arora, C Bano, M

Grundy, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2023, 143
Issue Date:: 2023-08-01

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Field	Value	Language
dc.contributor.author	Ahmad, K
dc.contributor.author	Abdelrazek, M
dc.contributor.author	Arora, C
dc.contributor.author	Bano, M https://orcid.org/0000-0002-1447-9521
dc.contributor.author	Grundy, J
dc.date.accessioned	2024-03-21T23:45:18Z
dc.date.available	2024-03-21T23:45:18Z
dc.date.issued	2023-08-01
dc.identifier.citation	Applied Soft Computing, 2023, 143
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/176992
dc.description.abstract	Context: Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google, Microsoft, and Apple have provided a suite of recent guidelines to assist engineering teams in building human-centered AI systems. Objective: The practices currently adopted by practitioners for developing such systems, especially during Requirements Engineering (RE), are little studied and reported to date. Method: This paper presents the results of a survey conducted to understand current industry practices in RE for AI (RE4AI) and to determine which key human-centered AI guidelines should be followed. Our survey is based on mapping existing industrial guidelines, best practices, and efforts in the literature. Results: We surveyed 29 professionals and found most participants agreed that all the human-centered aspects we mapped should be addressed in RE. Further, we found that most participants were using UML or Microsoft Office to present requirements. Conclusion: We identify that most of the tools currently used are not equipped to manage AI-based software, and the use of UML and Office may pose issues with the quality of requirements captured for AI. Also, all human-centered practices mapped from the guidelines should be included in RE.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2023.110421
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4903 Numerical and computational mathematics
dc.title	Requirements practices and gaps when engineering human-centered Artificial Intelligence systems
dc.type	Journal Article
utslib.citation.volume	143
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-21T23:45:16Z
pubs.publication-status	Published
pubs.volume	143

Abstract:

Context: Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google, Microsoft, and Apple have provided a suite of recent guidelines to assist engineering teams in building human-centered AI systems. Objective: The practices currently adopted by practitioners for developing such systems, especially during Requirements Engineering (RE), are little studied and reported to date. Method: This paper presents the results of a survey conducted to understand current industry practices in RE for AI (RE4AI) and to determine which key human-centered AI guidelines should be followed. Our survey is based on mapping existing industrial guidelines, best practices, and efforts in the literature. Results: We surveyed 29 professionals and found most participants agreed that all the human-centered aspects we mapped should be addressed in RE. Further, we found that most participants were using UML or Microsoft Office to present requirements. Conclusion: We identify that most of the tools currently used are not equipped to manage AI-based software, and the use of UML and Office may pose issues with the quality of requirements captured for AI. Also, all human-centered practices mapped from the guidelines should be included in RE.

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http://hdl.handle.net/10453/176992