Examining Factors of Human-AI Trust: Comparing Human- and AI-Related Factors

With the development of artificial intelligence (AI) technology, human-machine collaboration (HMC) plays an important role in enhancing construction safety. Human trust in AI is the key to the successful implementation of HMC. In this study, the effects of human-related factors (gender, technology acceptance) and AI-related factors (accuracy) on trust were measured and analyzed with controlled experiments simulating a tower crane operation scenario. Twenty-four college students were recruited for the experiment and randomly assigned to two false alarm rate conditions. A remote-controlled tower crane model toy was used to simulate a lifting task. A tablet computer was used to simulate an intrusion alarm monitoring system. Users' initial technical acceptance of the system was assessed via a questionnaire. Subjects' trust scores at the end of each alarm were measured using a trust rating scale. The statistical methods of t-test and two-way ANOVA were used to test the significant relationship between false alarm rate, gender, technology acceptance and trust score. The results show that the false alarm rate is a key factor affecting trust, while gender and technology acceptance and their interaction effects with the false alarm rate are not significant. The study emphasizes the importance of reducing false alarms and improving AI accuracy to enhance user trust.