Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis

Transparency remains critical but insufficient: While construction practitioners recognized the importance of understanding how AI-powered cobots work (addressing the "black-box nature" problem), the study found that transparency alone doesn't build trust—financial viability and change management concerns are equally significant barriers.

Trust extends beyond technical performance: The research identified that construction workers and managers don't evaluate AI cobots purely on technical merit. Factors like cost considerations and uncertainty about workplace disruption were found to be substantial trust obstacles, suggesting that responsible AI deployment requires attention to broader organizational and economic concerns.

Field expertise reveals gaps in AI design: By interviewing actual construction practitioners rather than relying solely on theoretical frameworks, the study uncovered trust factors that literature reviews had missed, indicating that AI developers may be overlooking real-world concerns when designing collaborative robots without sufficient input from end users.

Psychological factors matter in construction automation: The paper highlights that introducing AI-powered cobots involves "unknown psychological aspects," suggesting that worker anxiety, job security concerns, and adaptation challenges are legitimate trust issues that need to be addressed through change management strategies, not just technical improvements.

Customized trust-building is necessary: The findings suggest that responsible AI implementation in construction cannot follow a one-size-fits-all approach; stakeholders have diverse concerns that require tailored solutions addressing both technical transparency and practical workplace considerations.