Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural
networks that first predict labels for human-interpretable concepts relevant to
the prediction task, and then predict the final label based on the concept
label predictions.We extend CBMs to interactive prediction settings where the
model can query a human collaborator for the label to some concepts. We develop
an interaction policy that, at prediction time, chooses which concepts to
request a label for so as to maximally improve the final prediction. We
demonstrate thata simple policy combining concept prediction uncertainty and
influence of the concept on the final prediction achieves strong performance
and outperforms a static approach proposed in Koh et al. (2020) as well as
active feature acquisition methods proposed in the literature. We show that the
interactiveCBM can achieve accuracy gains of 5-10% with only 5 interactions
over competitive baselines on the Caltech-UCSDBirds, CheXpert and OAI datasets.

该研究提出一种交互式的 CBMs 模型，通过在预测过程中询问人类协作者对于某些概念的标签来提升最终预测准确率，并通过性能比较证明该模型相较于其他方法更为优越。