1. Adeli, E., et al.: Representation learning with statistical independence to mitigate bias. In: IEEE/CVF Winter Conference on Applications of Computer Vision (2021)
2. Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: IEEE Winter Conference on Applications of Computer Vision (2018)
3. Creager, E., et al.: Flexibly fair representation learning by disentanglement. In: International Conference on Machine Learning, pp. 1436–1445. PMLR (2019)
4. Dullerud, N., Roth, K., Hamidieh, K., Papernot, N., Ghassemi, M.: Is fairness only metric deep? Evaluating and addressing subgroup gaps in deep metric learning. In: The International Conference on Learning Representations (2022)
5. Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Algorithmic encoding of protected characteristics in image-based models for disease detection. arXiv preprint arXiv:2110.14755 (2021)