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dc.contributor.authorLove, Peter
dc.contributor.authorFang, Weili
dc.contributor.authorMatthews, Jane
dc.contributor.authorPorter, Stuart
dc.contributor.authorLuo, Hanbin
dc.contributor.authorDing, Lieyun
dc.date.accessioned2022-11-24T07:09:10Z
dc.date.available2022-11-24T07:09:10Z
dc.identifier.citationLove, P.E.D. and Fang, W. and Matthews, J. and Porter, S. and Luo, H. and Ding, L. Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/89696
dc.description.abstract

Explainable artificial intelligence has received limited attention in construction despite its growing importance in various other industrial sectors. In this paper, we provide a narrative review of XAI to raise awareness about its potential in construction. Our review develops a taxonomy of the XAI literature comprising its precepts and approaches. Opportunities for future XAI research focusing on stakeholder desiderata and data and information fusion are identified and discussed. We hope the opportunities we suggest stimulate new lines of inquiry to help alleviate the scepticism and hesitancy toward AI adoption and integration in construction.

dc.subjectcs.AI
dc.subjectcs.AI
dc.subjectH.0; H.4; J.0
dc.titleExplainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction
dc.typeJournal Article
dc.date.updated2022-11-24T07:09:08Z
curtin.departmentSchool of Civil and Mechanical Engineering
curtin.accessStatusIn process
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidLove, Peter [0000-0002-3239-1304]
curtin.contributor.researcheridLove, Peter [D-7418-2017]
curtin.contributor.scopusauthoridLove, Peter [7101960035]


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