  Explanatory Interactive Learning (XIL) collects user feedback on visual model
explanations to implement a Human-in-the-Loop (HITL) based interactive learning
scenario. Different user feedback types will have different impacts on user
experience and the cost associated with collecting feedback since different
feedback types involve different levels of image annotation. Although XIL has
been used to improve classification performance in multiple domains, the impact
of different user feedback types on model performance and explanation accuracy
is not well studied. To guide future XIL work we compare the effectiveness of
two different user feedback types in image classification tasks: (1)
instructing an algorithm to ignore certain spurious image features, and (2)
instructing an algorithm to focus on certain valid image features. We use
explanations from a Gradient-weighted Class Activation Mapping (GradCAM) based
XIL model to support both feedback types. We show that identifying and
annotating spurious image features that a model finds salient results in
superior classification and explanation accuracy than user feedback that tells
a model to focus on valid image features.
