Beyond Binary Decisions: Evaluating the Effects of AI Error Type on Trust and Performance in AI-Assisted Tasks

Objective We investigated how various error patterns from an AI aid in the nonbinary decision scenario influence human operators’ trust in the AI system and their task performance. Background Existing research on trust in automation/autonomy predominantly uses the signal detection theory (SDT) to model autonomy performance. The SDT classifies the world into binary states and hence oversimplifies the interaction observed in real-world scenarios. Allowing multi-class classification of the world reveals intriguing error patterns previously unexplored in prior literature. Method Thirty-five participants completed 60 trials of a simulated mental rotation task assisted by an AI with 70–80% reliability. Participants’ trust in and dependence on the AI system and their performance were measured. By combining participants’ initial performance and the AI aid’s performance, five distinct patterns emerged. Mixed-effects models were built to examine the effects of different patterns on trust adjustment, performance, and reaction time. Results Varying error patterns from AI impacted performance, reaction times, and trust. Some AI errors provided false reassurance, misleading operators into believing their incorrect decisions were correct, worsening performance and trust. Paradoxically, some AI errors prompted safety checks and verifications, which, despite causing a moderate decrease in trust, ultimately enhanced overall performance. Conclusion The findings demonstrate that the types of errors made by an AI system significantly affect human trust and performance, emphasizing the need to model the complicated human–AI interaction in real life. Application These insights can guide the development of AI systems that classify the state of the world into multiple classes, enabling the operators to make more informed and accurate decisions based on feedback.