Safeguarding the Construction Industry: Interrogating and Mitigating Emerging Risks from AI in Construction

Authors: A. Elagez, R. Monib

Abstract:

This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, with two distinct surveys (n = 15 and n = 45), and semi-structured interviews with four industry professionals, this research reveals significant disparities in AI adoption readiness between research and industry sectors. Survey results showed 87% familiarity with AI among research professionals compared to only 18% among industry practitioners, with corresponding adoption rates of 67% and 11% respectively. The study identified key barriers to AI adoption, with lack of awareness, insufficient knowledge and skills, and high implementation costs ranking as the most significant challenges. The most critical risks associated with AI use were identified as data security/privacy, lack of human control, algorithmic bias, and accountability. Additionally, the research revealed that 67% of research-oriented respondents believed Trustworthy AI could help manage risks, while 75% supported Explainable AI as a risk mitigation tool. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: Applications of artificial intelligence in construction, artificial intelligence ethics and bias, artificial intelligence risks in construction, risk management.

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