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Autonomous agents are increasingly becoming construction workers’ teammates, making them an integral part of tomorrow’s construction industry. Although many expect that worker–autonomy teaming will enhance construction efficiency, the presence of auto-agents, or robots necessitates an appropriate level of trust-building between workers and their autonomous counterparts, especially because these auto-agents’ perfection still cannot be guaranteed. Although researchers have widely explored human–autonomy trust in various domains—such as manufacturing and the military—discussion of this teaming dynamic within the construction sector is still nascent. To address this gap, this paper simulated a futuristic bricklaying task to (1) examine whether identifying autonomous agents’ physical and informational failures and risk perception affect workers’ trust levels, and (2) investigate workers’ neuropsychophysiological responses as a measure of trust levels toward robots, especially when autonomous agents are faulty. Results indicate that (1) identification of both types of failures and high-risk perception significantly reduce workers’ trust in autonomous agents, and the nuances of workers’ responses to both types of failures were discerned; and (2) brain activation correlates with trust changes. The findings suggest that workers’ unfamiliarity with autonomous technologies, coupled with fast-growing interest in adopting them, may leave workers at risk of improper trust transfer or overtrust in the autonomous agents. This study contributes to an expanding exploration of worker–autonomy trust in construction and calls for further investigations into effective approaches for auto-agents to communicate their physical and informational failures and to help workers recover and repair trust.
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Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions
With the construction sector primed to incorporate such advanced technologies as artificial intelligence (AI), robots, and machines, these advanced tools will require a deep understanding of human–robot trust dynamics to support safety and productivity. Although other disciplines have broadly investigated human trust-building with robots, the discussion within the construction domain is still nascent, raising concerns because construction workers are increasingly expected to work alongside robots or cobots, and to communicate and interact with drones. Without a better understanding of how construction workers can appropriately develop and calibrate their trust in their robotic counterparts, the implementation of advanced technologies may raise safety and productivity issues within these already-hazardous jobsites. Consequently, this study conducted a systematic review of the human–robot trust literature to (1) understand human–robot trust-building in construction and other domains; and (2) establish a roadmap for investigating and fostering worker–robot trust in the construction industry. The proposed worker–robot trust-building roadmap includes three phases: static trust based on the factors related to workers, robots, and construction sites; dynamic trust understood via measuring, modeling, and interpreting real-time trust behaviors; and adaptive trust, wherein adaptive calibration strategies and adaptive training facilitate appropriate trust-building. This roadmap sheds light on a progressive procedure to uncover the appropriate trust-building between workers and robots in the construction industry.
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- Award ID(s):
- 2128970
- PAR ID:
- 10656041
- Publisher / Repository:
- ASCE
- Date Published:
- Journal Name:
- Journal of Computing in Civil Engineering
- Volume:
- 38
- Issue:
- 3
- ISSN:
- 0887-3801
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1061/JCCEE5.CPENG-5656
