  Deep Neural Networks are prone to learning spurious correlations embedded in
the training data, leading to potentially biased predictions. This poses risks
when deploying these models for high-stake decision-making, such as in medical
applications. Current methods for post-hoc model correction either require
input-level annotations which are only possible for spatially localized biases,
or augment the latent feature space, thereby hoping to enforce the right
reasons. We present a novel method for model correction on the concept level
that explicitly reduces model sensitivity towards biases via gradient
penalization. When modeling biases via Concept Activation Vectors, we highlight
the importance of choosing robust directions, as traditional regression-based
approaches such as Support Vector Machines tend to result in diverging
directions. We effectively mitigate biases in controlled and real-world
settings on the ISIC, Bone Age, ImageNet and CelebA datasets using VGG, ResNet
and EfficientNet architectures. Code is available on
https://github.com/frederikpahde/rrclarc.
