Perceptive Biases in Construction Mediation: Evidence and Application of Artificial Intelligence

Abstract

In light of advancements in big data analysis and artificial intelligence (AI), there are emerging opportunities to harness these technologies to address perceptive biases. This study examines the potential perceptive biases that may arise when construction mediation is quasi-imposed on the disputing parties. This can happen when mediation attempts are stipulated in the construction contract or court-directed. It is argued that, under such circumstances, a negative perception might arise over whether a bona fide mediation can be realised. Concerns include the fairness and timeliness of the process, as well as the practice of opportunistic mediating behaviours. With data collected from practising construction mediation practitioners in Hong Kong, the constructs of Perceptions of Bona Fide Mediation, Quasi-Imposition, and Positive Mediation Outcomes were first developed. Applying partial least square structural equation modelling to the relationship frameworks of the constructs, it was found that quasi-imposition is not as damaging as envisaged as far as having a bona fide mediation and attaining positive mediation outcomes are concerned. Moreover, a negative perception of the fairness and timeliness of the quasi-imposed mediation would jeopardise the integrity of a bona fide mediation. In this regard, utilizing NLP and machine learning algorithms offers a pioneering AI-driven approach to informing mediating parties, as well as reminding mediators to uphold the fairness and timeliness of the process for the purposes of reaching positive mediation outcomes.