mlsanityData to 7 Oct 2026

Trust in AI and Implications for the AEC Research: A Literature Analysis

arXiv 2203.03847 ↗First posted 8 Mar 2022cs.HC cs.AI cs.ROcited by 4

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Abstract (arXiv)

Engendering trust in technically acceptable and psychologically embraceable systems requires domain-specific research to capture unique characteristics of the field of application. The architecture, engineering, and construction (AEC) research community has been recently harnessing advanced solutions offered by artificial intelligence (AI) to improve project workflows. Despite the unique characteristics of work, workers, and workplaces in the AEC industry, the concept of trust in AI has received very little attention in the literature. This paper presents a comprehensive analysis of the academic literature in two main areas of trust in AI and AI in the AEC, to explore the interplay between AEC projects unique aspects and the sociotechnical concepts that lead to trust in AI. A total of 490 peer-reviewed scholarly articles are analyzed in this study. The main constituents of human trust in AI are identified from the literature and are characterized within the AEC project types, processes, and technologies.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 37 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

6 of 37 references.

References · 6

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  3. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  4. 2020Making Fair ML Software using Trustworthy Explanationcited by 24
  5. 2020Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Contextcited by 7
  6. 2019AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributescited by 5

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Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.