Trust in AI and Implications for the AEC Research: A Literature Analysis
It addresses the lack of research on trust in AI for the AEC domain, which is incremental as it synthesizes existing literature rather than proposing new methods.
This paper analyzed 490 scholarly articles to explore how trust in AI applies to the architecture, engineering, and construction (AEC) industry, identifying key trust constituents specific to AEC projects and processes.
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.