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.

通过构建和解释高层概念的预测，概念瓶颈模型（CBMs）解决了神经架构的不透明性。我们提出了一种新的 CBM 基于架构和训练范式的模型，称为 Intervention-aware Concept Embedding models（IntCEMs），通过在训练时学习概念干预策略，有效地选择和接受概念干预，从而在测试时显著提高性能。我们的实验证明，当提供测试时概念干预时，IntCEMs 明显优于最先进的概念可解释模型，验证了我们方法的有效性。

学会接受帮助：干预感知的概念嵌入模型

Learning to Receive Help: Intervention-Aware Concept Embedding Models

Deploying AI-powered systems requires trustworthy models supporting effective
human interactions, going beyond raw prediction accuracy. Concept bottleneck
models promote trustworthiness by conditioning classification tasks on an
intermediate level of human-like concepts. This enables human interventions
which can correct mispredicted concepts to improve the model's performance.
However, existing concept bottleneck models are unable to find optimal
compromises between high task accuracy, robust concept-based explanations, and
effective interventions on concepts -- particularly in real-world conditions
where complete and accurate concept supervisions are scarce. To address this,
we propose Concept Embedding Models, a novel family of concept bottleneck
models which goes beyond the current accuracy-vs-interpretability trade-off by
learning interpretable high-dimensional concept representations. Our
experiments demonstrate that Concept Embedding Models (1) attain better or
competitive task accuracy w.r.t. standard neural models without concepts, (2)
provide concept representations capturing meaningful semantics including and
beyond their ground truth labels, (3) support test-time concept interventions
whose effect in test accuracy surpasses that in standard concept bottleneck
models, and (4) scale to real-world conditions where complete concept
supervisions are scarce.

该研究提出了一种新的概念限制模型 —— 概念嵌入模型，学习解释性高维概念表示，支持人类干预和提高模型性能，这是一个在真实世界条件下优化准确性、鲁棒性和解释能力之间权衡的方案。