Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery (2407.14499v2)

Abstract: Concept Bottleneck Models (CBMs) have recently been proposed to address the 'black-box' problem of deep neural networks, by first mapping images to a human-understandable concept space and then linearly combining concepts for classification. Such models typically require first coming up with a set of concepts relevant to the task and then aligning the representations of a feature extractor to map to these concepts. However, even with powerful foundational feature extractors like CLIP, there are no guarantees that the specified concepts are detectable. In this work, we leverage recent advances in mechanistic interpretability and propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm: instead of pre-selecting concepts based on the downstream classification task, we use sparse autoencoders to first discover concepts learnt by the model, and then name them and train linear probes for classification. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model. We perform a comprehensive evaluation across multiple datasets and CLIP architectures and show that our method yields semantically meaningful concepts, assigns appropriate names to them that make them easy to interpret, and yields performant and interpretable CBMs. Code available at https://github.com/neuroexplicit-saar/discover-then-name.

Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery

The paper "Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery" presents an insightful contribution to the field of interpretable machine learning by proposing a novel method for constructing Concept Bottleneck Models (CBMs). The authors introduce a strategy that diverges from traditional CBM construction by automatically discovering and naming concepts within neural models, particularly those derived from the CLIP architecture, in a task-agnostic manner. This methodology, termed DN-CBM, leverages sparse autoencoders to effectively disentangle complex neural representations into semantically meaningful, human-interpretable components.

Summary and Contributions

The core innovation of DN-CBM lies in its inversion of the traditional CBM paradigm, which typically relies on pre-selected, task-relevant concepts. Instead, this method utilizes sparse autoencoders to autonomously extract latent concepts from pre-trained models such as CLIP, which are then named using a large vocabulary of text embeddings. This approach has several advantages, including the elimination of the need for task-specific concept selection and the ability to generalize across different datasets without requiring specific concept annotations.

1. Automated Concept Discovery: The paper outlines the process of utilizing sparse autoencoders to extract concepts from CLIP's vision representations. This process involves training the autoencoders to produce sparse, high-dimensional latent spaces where individual neurons align with distinct, disentangled concepts.
2. Concept Naming via Text Embeddings: Once the concepts are extracted, the method employs the text embeddings from CLIP to automatically assign names to these concepts. This naming process involves matching the extracted concept vectors to the closest text embeddings, thus ensuring that the names are semantically linked to the concepts they represent.
3. Task-Agnostic Construction of CBMs: After naming, the extracted concepts are used to train linear classifiers for various datasets. Notably, the concept extraction and naming are performed independently of the downstream tasks, enabling the creation of a universal concept bottleneck layer that can be utilized for multiple classification tasks.
4. Empirical Evaluation: The authors conduct extensive experiments across diverse datasets, demonstrating that DN-CBM achieves competitive accuracy while maintaining interpretability. The method is benchmarked against existing CBM approaches and shows improvements in several instances, particularly highlighting its robustness and task-agnostic capabilities.

Implications and Future Work

The implications of this research are manifold. From a theoretical standpoint, DN-CBM provides a significant shift towards more generalized interpretable models, reducing reliance on task-specific knowledge and improving the scalability of CBMs. Practically, the method offers a promising avenue for deploying interpretable models in real-world applications where access to task-specific concept annotations is limited.

Future research could explore the refinement of this approach by increasing the granularity of the discovered concepts, potentially by enhancing the vocabulary used for naming or by training on larger and more diverse datasets. Moreover, addressing the issue of concept correlation and spurious activation in neural models—highlighted in the paper as a potential point of failure—could further enhance the robustness and fidelity of CBM explanations.

In conclusion, the "Discover-then-Name" approach represents a substantial advancement in creating interpretable AI models by harnessing the capabilities of automated concept discovery and naming. This innovative framework not only challenges existing paradigms but also establishes a robust platform for future explorations in the pursuit of inherently interpretable neural networks.