mlsanityData to 7 Oct 2026

Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction

arXiv 2211.06579 ↗First posted 12 Nov 2022cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 127 references that are in the map. First posted on arXiv 12 Nov 2022.

Builds on

25 of 127 references.

References · 25

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  6. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  7. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  8. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  9. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  10. 2014Understanding Deep Image Representations by Inverting Themcited by 399
  11. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  12. 2019Interpretable machine learning: definitions, methods, and applicationscited by 303
13 more
  1. 2016Synthesizing the preferred inputs for neurons in neural networks via deep generator networkscited by 181
  2. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  3. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  4. 2020Principles and Practice of Explainable Machine Learningcited by 99
  5. 2017Explaining Recurrent Neural Network Predictions in Sentiment Analysiscited by 87
  6. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  7. 2017The Promise and Peril of Human Evaluation for Model Interpretabilitycited by 52
  8. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  9. 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
  10. 2019A Survey of Data Fusion in Smart City Applicationscited by 10
  11. 2021Explainable Artificial Intelligence (XAI): An Engineering Perspectivecited by 6
  12. 2021Desiderata for Explainable AI in statistical production systems of the European Central Bankcited by 5
  13. 2020Black Magic in Deep Learning: How Human Skill Impacts Network Trainingcited by 3

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Are Linear Regression Models White Box and Interpretable?cited by 1
  2. 2024Explainable AI Methods for Multi-Omics Analysis: A Surveycited by 0
  3. 2025Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performancecited by 0
  4. 2025Unlocking Biomedical Insights: Hierarchical Attention Networks for High-Dimensional Data Interpretationcited by 0