Understanding Human Acceptance of Explainable AI in Automated Aesthetic Evaluation: A Multi-Study Investigation of Advice Taking and Explanation Requirements
Explainable Artificial Intelligence (XAI) has been increasingly applied in image aesthetic quality evaluation. However, it remains unclear how perceived explainability influences users’ decision adoption and what specific explanation requirements shape this perception. This study develops and tests a multi-stage model of XAI-supported human–AI decision-making in aesthetic evaluation based on Task–Technology Fit theory with a self-developed AI aesthetic evaluation system and PLS-SEM analysis (N = 494). Our findings reveal that perceived explainability does not exert a direct effect on perceived fairness, but influences it indirectly through two underlying psychological mechanisms: functional understanding and affective satisfaction. Furthermore, perceived fairness serves as a critical mediating mechanism linking explainability perceptions to decision outcomes. Extending this framework, a second online questionnaire study(N = 313) investigates the specific explanation requirements of the users in AI-assisted aesthetic evaluation. The results reveal seven categories of explanation needs and develop a corresponding question bank to guide the design and evaluation of XAI systems. This study contributes to a unified theoretical understanding of explainable human–AI decision-making in aesthetic quality evaluation and provides practical guidance for researchers, developers, and organizations designing XAI-assisted aesthetic quality evaluation systems.