Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework

• URL: http://arxiv.org/abs/2211.06561v1
• Date: Sat, 12 Nov 2022 03:50:14 GMT
• Title: Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
• Authors: Peter ED Love, Jane Matthews, Weili Fang, Stuart Porter, Hanbin Luo and Lieyun Ding
• Abstract summary: We develop a content, context, process, and outcome evaluation framework that can be used to justify the adoption and effective management of XAI. 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.
• Score: 1.3375143521862154
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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