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Current research on Explainable AI (XAI) heavily targets on expert users(data scientists or AI developers). However, increasing importance has beenargued for making AI more understandable to nonexperts, who are expected toleverage AI techniques, but have limited knowledge about AI. We present amobile application to support nonexperts to interactively make sense ofConvolutional Neural Networks (CNN); it allows users to play with a pretrainedCNN by taking pictures of their surrounding objects. We use an up-to-date XAItechnique (Class Activation Map) to intuitively visualize the model's decision(the most important image regions that lead to a certain result). Deployed in auniversity course, this playful learning tool was found to support designstudents to gain vivid understandings about the capabilities and limitations ofpretrained CNNs in real-world environments. Concrete examples of students'playful explorations are reported to characterize their sensemaking processesreflecting different depths of thought.

Explainability via Interactivity? Supporting Nonexperts' Sensemaking of Pretrained CNN by Interacting with Their Daily Surroundings