  Motivation: Traditional image attribution methods struggle to satisfactorily
explain predictions of neural networks. Prediction explanation is important,
especially in medical imaging, for avoiding the unintended consequences of
deploying AI systems when false positive predictions can impact patient care.
Thus, there is a pressing need to develop improved models for model
explainability and introspection. Specific problem: A new approach is to
transform input images to increase or decrease features which cause the
prediction. However, current approaches are difficult to implement as they are
monolithic or rely on GANs. These hurdles prevent wide adoption. Our approach:
Given an arbitrary classifier, we propose a simple autoencoder and gradient
update (Latent Shift) that can transform the latent representation of a
specific input image to exaggerate or curtail the features used for prediction.
We use this method to study chest X-ray classifiers and evaluate their
performance. We conduct a reader study with two radiologists assessing 240
chest X-ray predictions to identify which ones are false positives (half are)
using traditional attribution maps or our proposed method. Results: We found
low overlap with ground truth pathology masks for models with reasonably high
accuracy. However, the results from our reader study indicate that these models
are generally looking at the correct features. We also found that the Latent
Shift explanation allows a user to have more confidence in true positive
predictions compared to traditional approaches (0.15$\pm$0.95 in a 5 point
scale with p=0.01) with only a small increase in false positive predictions
(0.04$\pm$1.06 with p=0.57).
  Accompanying webpage: https://mlmed.org/gifsplanation
  Source code: https://github.com/mlmed/gifsplanation
