<p>Artificial intelligence (AI) is rapidly integrating into infrastructure planning, design, construction, management, and operation. This encompasses the use of AI-powered, intelligent systems to process vast amounts of data and support human decision-making concerning urban development, architecture, transportation systems, housing, energy efficiency, and sustainability of the built environment. AI integration into the built environment has also introduced new challenges with respect to transparency, accountability, fairness, and reliability in machine decision-making, and led to concerns about algorithmic bias, privacy, and ethical deployment of technology. A key barrier to formalizing trust in AI lies in the absence of consistent definitions of the main constructs of trust, and their interplay in trust formation and calibration. Trust formation involves the initial establishment of confidence in AI systems, while calibration refers to the ongoing alignment of trust with system performance and user experiences. Understanding how these processes interact within a decision context is crucial for developing robust frameworks that enhance trust in AI. In the built environment domain, in addition to technical capabilities of the AI systems, it is also essential to consider social, ethical, and legal dimensions, to ensure that the outcomes serve the best interests of humanity. This paper provides an overview of trust in AI systems, followed by describing findings from the literature through the lens of several built environment decision-making scenarios with the goal of creating a common understanding to support future research on human trust in intelligent systems, and how it may influence the quality and timeliness of resulting decision outcomes.</p>