AI anxiety: Explication and exploration of effect on state anxiety when interacting with AI doctors - Hyun Yang; S. Shyam Sundar (Computers in Human Behavior: Artificial Humans)
People often have anxiety toward artificial intelligence (AI) due to lack of transparency about its operation. This study explicates this anxiety by conceptualizing it as a trait, and examines its effect. It hypothesizes that users with higher AI (trait) anxiety would have higher state anxiety when interacting with an AI doctor, compared to those with lower AI (trait) anxiety, in part because it is a deviation from the status quo of being treated by a human doctor. As a solution, it hypothesizes that an AI doctor's explanations for its diagnosis would relieve patients' state anxiety. Furthermore, based on the status quo bias theory and an adaptation of the theory of interactive media effects (TIME) for the study of human-AI interaction (HAII), this study hypothesizes that the affect heuristic triggered by state anxiety would mediate the causal relationship between the source cue of a doctor and user experience (UX) as well as behavioral intentions. A pre-registered 2 (human vs. AI) x 2 (explainable vs. non-explainable) experiment (N = 346) was conducted to test the hypotheses. Data revealed that AI (trait) anxiety is significantly associated with state anxiety. Additionally, data showed that an AI doctor's explanations for its diagnosis significantly reduce state anxiety in patients with high AI (trait) anxiety but increase state anxiety in those with low AI (trait) anxiety, but these effects of explanations are not significant among patients who interact with a human doctor. Theoretical and design implications of these findings and limitations of this study are discussed.