HYPOTHESIS AND THEORY article
Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1662185
Explicating the trust process for effective human interaction with artificial intelligence and machine learning systems
Provisionally accepted
- Air Force Research Laboratory, Dayton, United States
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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.
Keywords: Trust, human-machine interaction, artificial intelligence, machine learning, Explainable artificial intelligence
Received: 14 Jul 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Alarcon and Capiola. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Gene Michael Alarcon, gene.alarcon.1@us.af.mil
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