What do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis

Agathe Balayn, Panagiotis Soilis, Christoph Lofi, Jie Yang, Alessandro Bozzon
2021 Proceedings of the Web Conference 2021  
Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple visual concepts. In this paper, we introduce a scalable human-inthe-loop approach for global interpretability. Salient image areas identified by local
more » ... ability methods are annotated with semantic concepts, which are then aggregated into a tabular representation of images to facilitate automatic statistical analysis of model behavior. We show that this approach answers interpretability needs for both model validation and exploration, and provides semantically more diverse, informative, and relevant explanations while still allowing for scalable and cost-efficient execution.
doi:10.1145/3442381.3450069 fatcat:pbmvaeysnrh3tiz3ziar26zfdm