  Class activation maps (CAMs) explain convolutional neural network predictions
by identifying salient pixels, but they become misaligned and misleading when
explaining predictions on images under bias, such as images blurred
accidentally or deliberately for privacy protection, or images with improper
white balance. Despite model fine-tuning to improve prediction performance on
these biased images, we demonstrate that CAM explanations become more deviated
and unfaithful with increased image bias. We present Debiased-CAM to recover
explanation faithfulness across various bias types and levels by training a
multi-input, multi-task model with auxiliary tasks for CAM and bias level
predictions. With CAM as a prediction task, explanations are made tunable by
retraining the main model layers and made faithful by self-supervised learning
from CAMs of unbiased images. The model provides representative, bias-agnostic
CAM explanations about the predictions on biased images as if generated from
their unbiased form. In four simulation studies with different biases and
prediction tasks, Debiased-CAM improved both CAM faithfulness and task
performance. We further conducted two controlled user studies to validate its
truthfulness and helpfulness, respectively. Quantitative and qualitative
analyses of participant responses confirmed Debiased-CAM as more truthful and
helpful. Debiased-CAM thus provides a basis to generate more faithful and
relevant explanations for a wide range of real-world applications with various
sources of bias.
