  This paper introduces a novel technique called counterfactual knowledge
distillation (CFKD) to detect and remove reliance on confounders in deep
learning models with the help of human expert feedback. Confounders are
spurious features that models tend to rely on, which can result in unexpected
errors in regulated or safety-critical domains. The paper highlights the
benefit of CFKD in such domains and shows some advantages of counterfactual
explanations over other types of explanations. We propose an experiment scheme
to quantitatively evaluate the success of CFKD and different teachers that can
give feedback to the model. We also introduce a new metric that is better
correlated with true test performance than validation accuracy. The paper
demonstrates the effectiveness of CFKD on synthetically augmented datasets and
on real-world histopathological datasets.
