2022/12/19 by Courtney Ford, Mark T. Keane, Ford, Courtney +1 · 4 citations
Computer Science · Medicine · #68-11 #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #H.5.2 #Human-Computer Interaction (cs.HC) #I.2.1 #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2212.09342
openalex publication_date 2022/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high stakes, human decision making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N=96) on how peoples expertise in a domain affects their understanding of post-hoc explanations by example for a deep-learning, black box classifier. The results show that peoples understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada MNIST). The wider implications of these new findings for XAI strategies are discussed.