Concept-based models perform prediction using a set of concepts that are interpretable to stakeholders. However, such models often involve a fixed, large number of concepts, which may place a substantial cognitive load on stakeholders. We propose Selective COncept Models (SCOMs) which make predictions using only a subset of concepts and can be customised by stakeholders at test-time according to their preferences. We show that SCOMs only require a fraction of the total concepts to achieve optimal accuracy on multiple real-world datasets. Further, we collect and release a new dataset, CUB-Sel, consisting of human concept set selections for 900 bird images from the popular CUB dataset. Using CUB-Sel, we show that humans have unique individual preferences for the choice of concepts they prefer to reason about, and struggle to identify the most theoretically informative concepts. The customisation and concept selection provided by SCOM improves the efficiency of interpretation and intervention for stakeholders.

翻译：概念模型通过一组对利益相关者可解释的概念进行预测。然而，这类模型通常涉及固定且大量的概念，这可能会给利益相关者带来显著的认知负担。我们提出选择性概念模型（SCOMs），该模型仅使用概念子集进行预测，并允许利益相关者在测试时根据偏好进行定制化。我们证明，在多个真实世界数据集上，SCOMs仅需总概念的一小部分即可达到最优精度。此外，我们收集并发布了一个新数据集CUB-Sel，该数据集包含来自流行CUB数据集的900张鸟类图像的人类概念集选择结果。利用CUB-Sel，我们发现人类对偏好的推理概念具有独特的个性化选择倾向，且难以识别最具理论信息量的概念。SCOM提供的定制化与概念选择功能提升了利益相关者进行解释与干预的效率。