Exploring Counterfactual Alignment Loss towards Human-centered AI
Mingzhou Liu Xinwei Sun Ching-Wen Lee Yu Qiao Yizhou Wang
Deep neural networks have demonstrated impressive accuracy in supervised
learning tasks. However, their lack of transparency makes it hard for humans to
trust their results, especially in safe-critic domains such as healthcare. To
address this issue, recent explanation-guided learning approaches proposed to
align the gradient-based attention map to image regions annotated by human
experts, thereby obtaining an intrinsically human-centered model. However, the
attention map these methods are based on may fail to causally attribute the
model predictions, thus compromising their validity for alignment. To address
this issue, we propose a novel human-centered framework based on counterfactual
generation. In particular, we utilize the counterfactual generation's ability
for causal attribution to introduce a novel loss called the CounterFactual
Alignment (CF-Align) loss. This loss guarantees that the features attributed by
the counterfactual generation for the classifier align with the human
annotations. To optimize the proposed loss that entails a counterfactual
generation with an implicit function form, we leverage the implicit function
theorem for backpropagation. Our method is architecture-agnostic and, therefore
can be applied to any neural network. We demonstrate the effectiveness of our
method on a lung cancer diagnosis dataset, showcasing faithful alignment to
humans.