eXplainable Artificial Intelligence (XAI) for improving organisational regility.

Shafiabady, N; Hadjinicolaou, N; Hettikankanamage, N; MohammadiSavadkoohi, E; Wu, RMX; Vakilian, J

eXplainable Artificial Intelligence (XAI) for improving organisational regility.

Shafiabady, N Hadjinicolaou, N Hettikankanamage, N MohammadiSavadkoohi, E Wu, RMX Vakilian, J

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Publisher:: PUBLIC LIBRARY SCIENCE
Publication Type:: Journal Article
Citation:: PLoS One, 2024, 19, (4), pp. e0301429
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Shafiabady, N
dc.contributor.author	Hadjinicolaou, N
dc.contributor.author	Hettikankanamage, N
dc.contributor.author	MohammadiSavadkoohi, E
dc.contributor.author	Wu, RMX
dc.contributor.author	Vakilian, J
dc.contributor.editor	Akbar, S
dc.date.accessioned	2024-08-06T03:32:17Z
dc.date.available	2024-03-15
dc.date.available	2024-08-06T03:32:17Z
dc.date.issued	2024
dc.identifier.citation	PLoS One, 2024, 19, (4), pp. e0301429
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/180129
dc.description.abstract	Since the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PUBLIC LIBRARY SCIENCE
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0301429
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	COVID-19
dc.subject.mesh	Decision Making
dc.subject.mesh	Humans
dc.subject.mesh	Decision Making
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	COVID-19
dc.subject.mesh	Decision Making
dc.title	eXplainable Artificial Intelligence (XAI) for improving organisational regility.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
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	*
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	2024-08-06T03:32:11Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	4

Abstract:

Since the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.

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http://hdl.handle.net/10453/180129