Interpretability and explainability of neural networks is continuously
increasing in importance, especially within safety-critical domains and to
provide the social right to explanation. Concept based explanations align well
with how humans reason, proving to be a good way to explain models. Concept
Embedding Models (CEMs) are one such concept based explanation architectures.
These have shown to overcome the trade-off between explainability and
performance. However, they have a key limitation -- they require concept
annotations for all their training data. For large datasets, this can be
expensive and infeasible. Motivated by this, we propose Automatic Concept
Embedding Models (ACEMs), which learn the concept annotations automatically.

神经网络的可解释性和解释能力在特别是在安全关键领域和提供社会解释权方面不断增加。定概念解释在线合人类推理的方式上效果良好，是一种很好的解释模型的方法。概念嵌入模型是一种基于概念的解释架构，已经证明在解释能力和性能之间取得了平衡。然而，它们存在一个关键限制 -- 需要为所有训练数据提供概念注释。针对这一问题，我们提出了自动概念嵌入模型（ACEMs），可以自动学习概念注释。

自动概念嵌入模型 (ACEM): 无需训练时间概念，无问题！

Automatic Concept Embedding Model (ACEM): No train-time concepts, No  issue!

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。