Chao Wang
Pengcheng An
ABSTRACT
Current research on explainable AI (XAI) is primarily aimed at expert users (data scientists or AI developers). However, there is an increasing emphasis on making AI more understandable to non-experts who are expected to use AI techniques but have limited knowledge about AI. We propose a mobile application to help non-experts understand convolutional neural networks (CNN) in an interactive way; it allows users to taking pictures of surrounding objects and use pre-trained CNN to recognize it. We use the latest XAI (Class Activation Map) technology to visualize the model decision (the most important image area leading to a specific result). This playful learning tool was implemented in college courses and found to help design students gain a vivid understanding of the functions and limitations of pre-trained CNN in the real world. We thereby contribute an online tool that could be used for twofold purposes: first, it could help non-experts interactively learn how a pre-trained CNN works. Second, it can be used by researchers to probe and characterize the non-experts’ process of sensemaking, which could contribute insights into explainable AI design beyond expert users.
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- A Mobile Tool that Helps Nonexperts Make Sense of Pretrained CNN by Interacting with Their Daily Surroundings
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