  CNN image classifiers are widely used, thanks to their efficiency and
accuracy. However, they can suffer from biases that impede their practical
applications. Most existing bias investigation techniques are either
inapplicable to general image classification tasks or require significant user
efforts in perusing all data subgroups to manually specify which data
attributes to inspect. We present VisCUIT, an interactive visualization system
that reveals how and why a CNN classifier is biased. VisCUIT visually
summarizes the subgroups on which the classifier underperforms and helps users
discover and characterize the cause of the underperformances by revealing image
concepts responsible for activating neurons that contribute to
misclassifications. VisCUIT runs in modern browsers and is open-source,
allowing people to easily access and extend the tool to other model
architectures and datasets. VisCUIT is available at the following public demo
link: https://poloclub.github.io/VisCUIT. A video demo is available at
https://youtu.be/eNDbSyM4R_4.
