Responsible Artificial Intelligence in the Built Environment: Governance, Capability and Equitable Adoption

Artificial intelligence (AI) is reshaping the built environment, with growing applications across the project lifecycle. These range from generative design and cost estimation to safety monitoring, defect and cladding detection, progress tracking, and asset and facilities management, and are increasingly extended by generative and agentic systems. This is driven by the promise of significant gains in productivity, quality, and sustainability in project delivery. However, adoption is racing ahead of governance and capability, creating real risks. There are growing threats of tools being misapplied in high-consequence settings, opaque and unaccountable AI-assisted decisions, cognitive challenges such as over-reliance and deskilling, and a widening AI divide between those able to adopt responsibly and those left behind.

Extant studies have focused largely on the applications and technical performance of AI, while adoption remains low and unevenly distributed. These studies have emphasised workforce and capability shortages, weak data maturity, and unresolved governance, trust, and ethical concerns as dominant barriers to responsible adoption. This is further compounded by the existing digital divide in the sector, which widens beyond technology- or access-driven dimensions to encompass organisational, disciplinary, and regional disparities. With generative and agentic AI accelerating deployment faster than the sector's guardrails, the gap between capability and responsibility is widening. Professional bodies are increasingly signalling towards responsible AI, yet research examining governance, capability-building, accountability, and equity in the built environment remains limited and fragmented.

This special issue aims to assemble empirical, conceptual, and design-science research that advances both the theory and practice of Responsible AI adoption across the built environment. It shifts the focus from demonstrating what AI can do towards actionable responsibility: the governance mechanisms, capability-building, and equity conditions that determine whether AI delivers value in practice. The issue treats responsible AI as an AI-specific and built-environment-specific problem, rather than importing generic AI-ethics templates. It integrates the technical (how AI systems are built, validated, and assured) with the socio-organisational (how they are governed and made responsibly usable across people and organisations). It also engages the AI divide in its many dimensions, spanning organisations, disciplines, resources, and geographies.

Empirical, conceptual, review, and design-science contributions using qualitative, quantitative, and mixed methods, from across academia and practice, are welcome. The issue seeks to consolidate research-informed contributions that advance responsible AI governance, capability-building, and equitable adoption across the built environment. The papers will offer reusable frameworks, taxonomies, and evidence-based guidance rather than isolated demonstrations. We particularly encourage submissions that bridge the science-policy and policy-practice divide, and that foreground the responsible, inclusive, and accountable deployment of AI in the sector.

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/jedt

Author guidelines must be strictly followed. Please see the Emerald Publishing Author Guidelines: Emerald Publishing Author Guidelines

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to.”

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.