Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction
This work targets the construction industry by highlighting XAI's potential to address stakeholder needs and data challenges, but it is incremental as it primarily reviews existing literature without introducing new methods or results.
The paper addresses the limited attention to explainable artificial intelligence (XAI) in construction by providing a narrative review that develops a taxonomy of XAI precepts and approaches, and identifies opportunities for future research to stimulate inquiry and reduce skepticism toward AI adoption in the sector.
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.