Concept Bottleneck Models

Concept Bottleneck Models predict through an intermediate set of meaningful attributes, such as visual features or clinical findings, so people can examine how those concepts contribute to an output. Approaches include supervised concept predictors with simple classifiers, vision-language models that propose concepts, and structured bottlenecks that model concept relations, hierarchies, or spatial locations. The methods are tested in settings such as fine-grained image classification, medical diagnosis, text and graph prediction, and multimodal tasks, with evaluation increasingly examining concept accuracy and interventions as well as task accuracy. A central tension is that incomplete or noisy concepts can hurt predictions, while information leakage can preserve accuracy but make explanations or user interventions unreliable.