Concept Bottleneck Models can now work reliably across text and images by jointly addressing concept detection and information leakage—enabling interpretable AI without sacrificing accuracy.
This paper introduces f-CBM, a framework for building interpretable multimodal AI models that make predictions through human-understandable concepts. The key innovation is solving two problems simultaneously: accurately detecting concepts and preventing 'leakage' (where irrelevant information sneaks into predictions).