Research has found that many occupational accidents are foreseeable, being the result of people's unsafe behaviour from a retrospective point of view. The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which vary among individuals as a function of personality features. In this study, an innovative forecasting model, which employs machine learning algorithms, is developed to estimate construction workers' behavioural tendency based on the Big Five personality taxonomy. The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have practical implications. The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for decision making and safety management on construction sites.

Innovation, creativity, and competition are some of the fundamental underlying forces driving the advances in Artificial Intelligence (AI). This race for technological supremacy creates a complex ecology of choices that may lead to negative consequences, in particular, when ethical and safety procedures are underestimated or even ignored. Here we resort to a novel game theoretical framework to describe the ongoing AI bidding war, also allowing for the identification of procedures on how to influence this race to achieve desirable outcomes. By exploring the similarities between the ongoing competition in AI and evolutionary systems, we show that the timelines in which AI supremacy can be achieved play a crucial role for the evolution of safety prone behaviour and whether influencing procedures are required. When this supremacy can be achieved in a short term (near AI), the significant advantage gained from winning a race leads to the dominance of those who completely ignore the safety precautions to gain extra speed, rendering of the presence of reciprocal behavior irrelevant. On the other hand, when such a supremacy is a distant future, reciprocating on others' safety behaviour provides in itself an efficient solution, even when monitoring of unsafe development is hard. Our results suggest under what conditions AI safety behaviour requires additional supporting procedures and provide a basic framework to model them.