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。