Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
It addresses the need for structured evaluation of XAI in construction, an incremental step for industry practitioners.
The paper tackles the lack of evaluation frameworks for explainable artificial intelligence (XAI) in construction by developing a content, context, process, and outcome framework to guide adoption and management, providing a conceptual reference for realizing XAI benefits.
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.