A Systematic Literature Review of the Latest Advancements in XAI

Altukhi, ZMA; Pradhan, S; Aljohani, N

A Systematic Literature Review of the Latest Advancements in XAI

Altukhi, ZMA Pradhan, S Aljohani, N

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Technologies, 2025, 13, (3)
Issue Date:: 2025-03-01

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Field	Value	Language
dc.contributor.author	Altukhi, ZMA
dc.contributor.author	Pradhan, S
dc.contributor.author	Aljohani, N
dc.date.accessioned	2025-03-01T13:06:24Z
dc.date.available	2025-02-25
dc.date.available	2025-03-01T13:06:24Z
dc.date.issued	2025-03-01
dc.identifier.citation	Technologies, 2025, 13, (3)
dc.identifier.issn	2227-7080
dc.identifier.issn	2227-7080
dc.identifier.uri	http://hdl.handle.net/10453/185465
dc.description.abstract	This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Technologies
dc.relation.isbasedon	10.3390/technologies13030093
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4008 Electrical engineering
dc.title	A Systematic Literature Review of the Latest Advancements in XAI
dc.type	Journal Article
utslib.citation.volume	13
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 Professional Practice and Leadership
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-03-01T13:06:21Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	3

Abstract:

This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.

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http://hdl.handle.net/10453/185465