  Recent efforts in interpreting Convolutional Neural Networks (CNNs) focus on
translating the activation of CNN filters into stratified Answer Set
Programming (ASP) rule-sets. The CNN filters are known to capture high-level
image concepts, thus the predicates in the rule-set are mapped to the concept
that their corresponding filter represents. Hence, the rule-set effectively
exemplifies the decision-making process of the CNN in terms of the concepts
that it learns for any image classification task. These rule-sets help expose
and understand the biases in CNNs, although correcting the biases effectively
remains a challenge. We introduce a neurosymbolic framework called NeSyBiCor
for bias correction in a trained CNN. Given symbolic concepts that the CNN is
biased towards, expressed as ASP constraints, we convert the undesirable and
desirable concepts to their corresponding vector representations. Then, the CNN
is retrained using our novel semantic similarity loss that pushes the filters
away from the representations of concepts that are undesirable while pushing
them closer to the concepts that are desirable. The final ASP rule-set obtained
after retraining, satisfies the constraints to a high degree, thus showing the
revision in the knowledge of the CNN for the image classification task. We
demonstrate that our NeSyBiCor framework successfully corrects the biases of
CNNs trained with subsets of classes from the Places dataset while sacrificing
minimal accuracy and improving interpretability, by greatly decreasing the size
of the final bias-corrected rule-set w.r.t. the initial rule-set.
