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dc.contributor.authorLove, Peter
dc.contributor.authorMatthews, Jane
dc.contributor.authorFang, Weili
dc.contributor.authorPorter, Stuart
dc.contributor.authorLuo, Hanbin
dc.contributor.authorDing, Lieyun
dc.date.accessioned2022-11-24T07:07:48Z
dc.date.available2022-11-24T07:07:48Z
dc.identifier.citationLove, P.E.D. and Matthews, J. and Fang, W. and Porter, S. and Luo, H. and Ding, L. Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/89695
dc.description.abstract

Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no evaluation frameworks have been propagated to enable construction organisations to understand the what, why, how, and when of XAI. Our paper aims to fill this void by developing a content, context, process, and outcome evaluation framework that can be used to justify the adoption and effective management of XAI. After introducing and describing this novel framework, we discuss its implications for future research. While our novel framework is conceptual, it provides a frame of reference for construction organisations to make headway toward realising XAI business value and benefits.

dc.subjectcs.AI
dc.subjectcs.AI
dc.subjectH.0; H.4; J.0
dc.titleExplainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
dc.typeJournal Article
dc.date.updated2022-11-24T07:07:45Z
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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