  Model explanations such as saliency maps can improve user trust in AI by
highlighting important features for a prediction. However, these become
distorted and misleading when explaining predictions of images that are subject
to systematic error (bias). Furthermore, the distortions persist despite model
fine-tuning on images biased by different factors (blur, color temperature,
day/night). 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 explanation and bias level predictions. In simulation
studies, the approach not only enhanced prediction accuracy, but also generated
highly faithful explanations about these predictions as if the images were
unbiased. In user studies, debiased explanations improved user task
performance, perceived truthfulness and perceived helpfulness. Debiased
training can provide a versatile platform for robust performance and
explanation faithfulness for a wide range of applications with data biases.
