What's Missing in Requirements Engineering for Responsible AI?

Zowghi, D; Bano, M; Borg, M

What's Missing in Requirements Engineering for Responsible AI?

Zowghi, D

Bano, M

Borg, M

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Software, 2023, 40, (6), pp. 11-15
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155
dc.contributor.author	Bano, M https://orcid.org/0000-0002-1447-9521
dc.contributor.author	Borg, M
dc.date.accessioned	2024-03-28T02:30:48Z
dc.date.available	2024-03-28T02:30:48Z
dc.date.issued	2023-11-01
dc.identifier.citation	IEEE Software, 2023, 40, (6), pp. 11-15
dc.identifier.issn	0740-7459
dc.identifier.issn	1937-4194
dc.identifier.uri	http://hdl.handle.net/10453/177318
dc.description.abstract	The rapid evolution of artificial intelligence (AI) has catalyzed a multifaceted discourse in the software engineering (SE) community. The crux of this dialogue is to pinpoint the distinct attributes of AI systems that necessitate tailored SE methodologies. While classical SE techniques have proved effective across a spectrum of systems, there's an emerging consensus: AI introduces distinct challenges, compelling us to rethink some foundational principles of traditional SE.1 Central to AI systems is the imperative to design models, curate training datasets, govern system autonomy, and embed ethical guidelines. Two salient features of AI operations include continuous learning from evolving datasets and human feedback while dealing with the increased uncertainties and risks due to system autonomy.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Software
dc.relation.isbasedon	10.1109/MS.2023.3302934
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Software Engineering
dc.subject.classification	4612 Software engineering
dc.title	What's Missing in Requirements Engineering for Responsible AI?
dc.type	Journal Article
utslib.citation.volume	40
utslib.for	0803 Computer Software
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Research)
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	University of Technology Sydney/Strength - STEM Education Futures
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/DVC (Research)/Graduate Research School
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-28T02:30:45Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	40
utslib.citation.issue	6

Abstract:

The rapid evolution of artificial intelligence (AI) has catalyzed a multifaceted discourse in the software engineering (SE) community. The crux of this dialogue is to pinpoint the distinct attributes of AI systems that necessitate tailored SE methodologies. While classical SE techniques have proved effective across a spectrum of systems, there's an emerging consensus: AI introduces distinct challenges, compelling us to rethink some foundational principles of traditional SE.1 Central to AI systems is the imperative to design models, curate training datasets, govern system autonomy, and embed ethical guidelines. Two salient features of AI operations include continuous learning from evolving datasets and human feedback while dealing with the increased uncertainties and risks due to system autonomy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177318