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

arXiv: https://arxiv.org/abs/2203.03847 · HTML: https://mlsanity.com/paper/2203.03847/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Making Fair ML Software using Trustworthy Explanation](https://mlsanity.com/paper/2007.02893/) (2020)
- [Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context](https://mlsanity.com/paper/2007.08911/) (2020)
- [AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes](https://mlsanity.com/paper/1908.05972/) (2019)

## Built on by (most cited first, 4 of 4)

- [Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis](https://mlsanity.com/paper/2308.14846/) (2023)
- [LeanAI: A method for AEC practitioners to effectively plan AI implementations](https://mlsanity.com/paper/2306.16799/) (2023)
- [Assessing Trust in Construction AI-Powered Collaborative Robots using Structural Equation Modeling](https://mlsanity.com/paper/2308.14697/) (2023)
- [VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication](https://mlsanity.com/paper/2503.07279/) (2025)

## 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.