Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials
This study provides a systematic literature review on trustworthy artificial intelligence and robotics (AIR) and their implications for the architecture, engineering, and construction (AEC) industry. It identifies four main trust dimensions—Transparency and Interpretability, Reliability and Safety, Performance and Robustness, and Privacy and Security—and links them to existing and potential AEC applications. Finally, the paper outlines major future research directions to enhance technology adoption and build trust in AIR systems within the construction sector.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, scholars, and AEC practitioners interested in understanding trust dimensions and technology adoption drivers for artificial intelligence and robotics in construction.
What the document contains
The paper covers a systematic review of 484 peer-reviewed papers on trust in artificial intelligence and robotics, categorizing trust dimensions into transparency, reliability, performance, and privacy, and discussing their specific applications and barriers within the architecture, engineering, and construction (AEC) industry.
The document does not provide binding regulatory frameworks, legal enforcement mechanisms, or formal government policy mandates for artificial intelligence governance.
- Trust in artificial intelligence and robotics (AIR) is a critical requirement for user acceptance and technology adoption in the AEC industry.
- The literature review organizes trust into four primary dimensions: Transparency and Interpretability, Reliability and Safety, Performance and Robustness, and Privacy and Security.
- Small businesses comprise the vast majority of the AEC industry and tend to be laggards in technology adoption, making culture and trust-building essential.
- Human-robot collaboration in construction requires addressing both technical safety and psychological safety to prevent worker hesitancy or overtrust.
- Future research must focus on integrated interdisciplinary studies, trust calibration, human-centered trust, enhanced transparency and interpretability, and intuitive interfaces.
1. Introduction 2. Research Background 3. Trust Dimensions 4. Systematic Review Methodology 5. Trust in AIR and its Implications for AEC Adoption 6. Future Research 7. Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
1/5Clear responsibility for AI outcomes
Orr and Davis 2020
3.4. Privacy and Security (P&S)
screening score · 2026-10-23
Fairness
1/5Non-discrimination and equitable treatment
Chouldechova and Roth 2018
7. Conclusion
screening score · 2026-10-23
Human Oversight
3/5Humans remain in control of consequential decisions
In construction, HITL applications comprise the majority of the current applications. In one example, and with an emphasis on the unstructured, dynamic job site environment and conditions, Follini et al. (2020) proposed a collaborative robotic platform focused on HILT applications.
5.1.1. T&I in AEC AIR
screening score · 2026-10-23
Privacy
3/5Protection of personal data and private life
In the AEC literature, workers and client’s sensitive data vulnerable to privacy leaks (Choi, Hwang et al. 2017) as well as cybersecurity issues pertaining to the use of cameras, drones, and other sensing devices have been widely regarded as barriers to technology adoption
3.4. Privacy and Security (P&S)
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
In computer science, explainable AI (xAI) is a trending research topic (Fox, Long et al. 2017) . In construction, it is e specially important since technologies are often not developed in-house.
3.1. Transparency and Interpretability (T&I)
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
N/A
screening score · 2026-10-23
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
AI can also be used to predict safety measures such as injury severity, injury type, body p art impacted, and incident type in a construction project
5.2.1. R&S in AEC AIR
screening score · 2026-10-23