An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions

Nimmy, SF; Hussain, OK; Chakrabortty, RK; Hussain, FK; Saberi, M

An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions

Nimmy, SF Hussain, OK Chakrabortty, RK Hussain, FK Saberi, M

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2023, 271
Issue Date:: 2023-07-08

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Field	Value	Language
dc.contributor.author	Nimmy, SF
dc.contributor.author	Hussain, OK
dc.contributor.author	Chakrabortty, RK
dc.contributor.author	Hussain, FK
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.date.accessioned	2024-02-29T06:12:55Z
dc.date.available	2024-02-29T06:12:55Z
dc.date.issued	2023-07-08
dc.identifier.citation	Knowledge-Based Systems, 2023, 271
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/175974
dc.description.abstract	The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2023.110552
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions
dc.type	Journal Article
utslib.citation.volume	271
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
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	2024-02-29T06:12:52Z
pubs.publication-status	Published
pubs.volume	271

Abstract:

The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed.

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http://hdl.handle.net/10453/175974