Domain Experience and Expertise in Explainable AI Applications: A Bearing Fault Diagnosis Case Study

The importance of human-centred explainable artificial intelligence (XAI) has been widely recognised, leading to a growing focus on users and practitioners during the explanation design and deployment processes. Previous studies have identified that users with different domain expertise may have diverse needs for explainability. However, current XAI research often conflates a user's practical experience with domain expertise, ignoring the distinctions between the two; generally, experience relates to acquiring skill and insight through active participation or observation, while domain expertise denotes a high level of (often highly local and/or specific) knowledge. This paper investigates the impact of users' practical experience and domain expertise on how AI recommendations are considered in a high-risk decision-making context, using the example of ball bearing fault diagnosis in the manufacturing sector. As an interdisciplinary team of human-computer interaction (HCI) researchers and mechanical engineers, we co-design an XAI-based simulated ball bearing fault diagnostic task. We conduct task-led interviews with several professionals, structured around three distinct decision processes, and use an innovative sketch-based exercise to gather data to demonstrate how their decision-making behaviours change under ML recommendations and AI explanations. Our results show that highly experienced and knowledgeable practitioners understand but rely less on the explanations, while those with high experience but low expertise are more easily misled. Practitioners with high expertise but low experience trust XAI but struggle to use the explanations effectively. Based on these observations, we reflect on our methods and argue for considering both domain expertise and practical experience when designing and deploying AI explanations.