In AI We Trust? Effects of Agency Locus and Transparency on Uncertainty Reduction in Human–AI Interaction
Bingjie Liu
Abstract
Abstract Artificial intelligence (AI) is increasingly used to make decisions for humans. Unlike traditional AI that is programmed to follow human-made rules, machine-learning AI generates rules from data. These machine-generated rules are often unintelligible to humans. Will users feel more uncertainty about decisions governed by such rules? To what extent does rule transparency reduce uncertainty and increase users’ trust? In a 2 × 3 × 2 between-subjects online experiment, 491 participants interacted with a website that was purported to be a decision-making AI system. Three factors of the AI system were manipulated: agency locus (human-made rules vs. machine-learned rules), transparency (no vs. placebic vs. real explanations), and task (detecting fake news vs. assessing personality). Results show that machine-learning AI triggered less social presence, which increased uncertainty and lowered trust. Transparency reduced uncertainty and enhanced trust, but the mechanisms for this effect differed between the two types of AI.