Inclusive and Explainable AI Systems A Systematic Literature Review

Girard, A; Zowghi, D; Bano, M; Riziou, M-A

Inclusive and Explainable AI Systems A Systematic Literature Review

Girard, A Zowghi, D

Bano, M Riziou, M-A

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Publisher:: AIS
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual Hawaii International Conference on System Sciences, 2024, pp. 1297-1306
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Girard, A
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155
dc.contributor.author	Bano, M
dc.contributor.author	Riziou, M-A
dc.date	2024-01-03
dc.date.accessioned	2025-02-18T23:03:54Z
dc.date.available	2025-02-18T23:03:54Z
dc.date.issued	2024-01-01
dc.identifier.citation	Proceedings of the Annual Hawaii International Conference on System Sciences, 2024, pp. 1297-1306
dc.identifier.isbn	978-0-9981331-7-1
dc.identifier.issn	1530-1605
dc.identifier.uri	http://hdl.handle.net/10453/185180
dc.description.abstract	Explainable AI (XAI) plays a crucial role in enhancing transparency and providing rational explanations to support users of AI systems. Inclusive AI actively seeks to engage and represent individuals with diverse attributes who are affected by and contribute to the AI ecosystem. Both inclusion and XAI advocate for the active involvement of the users and stakeholders during the entire AI system lifecycle. However, the relationship between XAI and Inclusive AI has not been explored. In this paper, we present the results of a systematic literature review with the objective to explore this relationship in the recent AI research literature. We were able to identify 18 research articles on the topic. Our analysis focused on exploring approaches to (1) the human attributes and perspectives, (2) preferred explanation methods, and (3) human-AI interaction. Based on our findings, we identified potential future XAI research directions and proposed strategies for practitioners involved in the design and development of inclusive AI systems.
dc.language	en
dc.publisher	AIS
dc.relation.ispartof	Proceedings of the Annual Hawaii International Conference on System Sciences
dc.relation.ispartof	Annual Hawaii International Conference on System Sciences
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Inclusive and Explainable AI Systems A Systematic Literature Review
dc.type	Conference Proceeding
utslib.location.activity	Hawaii, USA
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-02-18T23:03:53Z
pubs.finish-date	2024-01-06
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2024-01-03
dc.location	USA

Abstract:

Explainable AI (XAI) plays a crucial role in enhancing transparency and providing rational explanations to support users of AI systems. Inclusive AI actively seeks to engage and represent individuals with diverse attributes who are affected by and contribute to the AI ecosystem. Both inclusion and XAI advocate for the active involvement of the users and stakeholders during the entire AI system lifecycle. However, the relationship between XAI and Inclusive AI has not been explored. In this paper, we present the results of a systematic literature review with the objective to explore this relationship in the recent AI research literature. We were able to identify 18 research articles on the topic. Our analysis focused on exploring approaches to (1) the human attributes and perspectives, (2) preferred explanation methods, and (3) human-AI interaction. Based on our findings, we identified potential future XAI research directions and proposed strategies for practitioners involved in the design and development of inclusive AI systems.

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http://hdl.handle.net/10453/185180