让可解释模型给出多种概念解释，供专家挑选最符合直觉的。
Diverse Concept Proposals for Concept Bottleneck Models
- 通过生成多种预测概念组合，提供多元解释路径
- 在真实医疗数据中无监督识别出5个预设概念中的4个
- 适合需要可解释性且关注专家直觉的医疗等高风险领域
概念瓶颈模型是可解释的预测模型，常用于医疗等需高度信任的领域。它们从数据中提取少量人类可理解的概念并据此做预测。但自动学习相关概念极具挑战：最具预测性的概念可能与专家直觉不符，导致可解释性失效且无法补救。本文提出的方法能发现多个具有预测能力的概念组合，提供多种替代解释，使人类专家可选择最契合自身认知的版本。在合成数据上，该方法能发现所有可能的概念表示；在电子健康记录（EHR）数据上，模型在无监督条件下成功识别出5个预设概念中的4个。
原文摘要 · Abstract (English)
Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data, which they then use to make predictions. Learning relevant concepts from data proves to be a challenging task. The most predictive concepts may not align with expert intuition, thus, failing interpretability with no recourse. Our proposed approach identifies a number of predictive concepts that explain the data. By offering multiple alternative explanations, we allow the human expert to choose the one that best aligns with their expectation. To demonstrate our method, we show that it is able discover all possible concept representations on a synthetic dataset. On EHR data, our model was able to identify 4 out of the 5 pre-defined concepts without supervision.
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