Explicating the trust process for effective human interaction with artificial intelligence and machine learning systems - Alarcon, Gene Michael; Capiola, August (Frontiers in Computer Science, 2025)
Artificial intelligence (AI) and machine learning (ML) are rapidly changing the landscape of almost every environment. Despite the burgeoning attention on this subject matter, limited human-centered research has focused on understanding how users interact with AI and ML to facilitate greater trust toward these systems, leveraging classic human-machine interaction principles to investigate human interaction with these emerging complex systems. The current paper incorporates literature from Social Psychology, Computer Science, Information Sciences, and Human Factors Psychology to create a single comprehensive model for understanding user interactions with AI/ML-enabled systems. This paper expands previous theoretical models by explicating transparency, incorporating individual differences in the information processing model of cognition, and summarizing the different attitudes and personality variables that can facilitate use and disuse of AI and ML. The theoretical model proposed explicitly demarcates the referent algorithm from the human user, detailing the processes that eventuate a user’s reliance on and compliance with an AI/ML-enabled system. Actual and potential applications of the literature review and theorized model are discussed.