Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 81 references that are in the map. First posted on arXiv 12 Nov 2022.
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References · 8
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
- 2020Principles and Practice of Explainable Machine Learningcited by 99
- 2019Towards Explainable Deep Neural Networks (xDNN)cited by 61
- 2020The Pragmatic Turn in Explainable Artificial Intelligence (XAI)cited by 54
- 2020Reviewing the Need for Explainable Artificial Intelligence (xAI)cited by 29
- 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
- 2020Black Magic in Deep Learning: How Human Skill Impacts Network Trainingcited by 4
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