  Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures
by constructing and explaining their predictions using a set of high-level
concepts. A special property of these models is that they permit concept
interventions, wherein users can correct mispredicted concepts and thus improve
the model's performance. Recent work, however, has shown that intervention
efficacy can be highly dependent on the order in which concepts are intervened
on and on the model's architecture and training hyperparameters. We argue that
this is rooted in a CBM's lack of train-time incentives for the model to be
appropriately receptive to concept interventions. To address this, we propose
Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based
architecture and training paradigm that improves a model's receptiveness to
test-time interventions. Our model learns a concept intervention policy in an
end-to-end fashion from where it can sample meaningful intervention
trajectories at train-time. This conditions IntCEMs to effectively select and
receive concept interventions when deployed at test-time. Our experiments show
that IntCEMs significantly outperform state-of-the-art concept-interpretable
models when provided with test-time concept interventions, demonstrating the
effectiveness of our approach.
