Concept Bottleneck Models (2007.04612v3)

Abstract: We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis? State-of-the-art models today do not typically support the manipulation of concepts like "the existence of bone spurs", as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity). We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts ("bone spurs") or bird attributes ("wing color"). These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time.

• The paper introduces a novel framework that uses human-understandable concepts as an intermediate step for accurate predictions.
• The paper demonstrates competitive accuracy and enables real-time expert intervention, particularly in high-stakes applications like medical diagnosis.
• The paper shows that concept bottleneck models maintain robust performance under covariate shifts, highlighting their value in creating transparent AI systems.

Concept Bottleneck Models: An In-depth Examination

The paper "Concept Bottleneck Models" investigates an approach to machine learning models that emphasizes interpretability through human-understandable concepts. This work revisits a structured model design where predictions are made via an intermediate representation aligned with human-specified concepts. This approach seeks to bridge the gap between complex models and human interpretability, providing a framework for interactive learning and intervention in high-stakes tasks like medical diagnosis.

Core Proposition

The central proposal is to leverage "concept bottleneck models" that predict a set of intermediate, human-defined concepts before making the final prediction. These models are trained using datasets annotated with both the target output and the corresponding concepts. By assessing their ability to predict directly based on human-understandable concepts, these models offer the advantage of being interpretable and modifiable in real-time by domain experts.

Significant Findings

1. Accuracy Competitiveness: The paper demonstrates that concept bottleneck models achieve task accuracy comparable to standard end-to-end models. On datasets like x-ray grading for osteoarthritis (OAI) and bird species identification (CUB), these models perform robustly, often matching or surpassing the predictive abilities of traditional deep learning models.
2. Intervention and Interpretability: A pivotal capability of these models is the potential for human intervention. Domain experts can directly modify concepts to adjust the model’s predictions, facilitating a practical interaction between human judgment and machine predictions. This is particularly beneficial in settings like healthcare, where altering the prediction based on nuanced understanding (e.g., correcting the model's misconception about a ‘bone spur’ in x-ray images) can significantly improve diagnostic accuracy.
3. Post-hoc Analysis Insights: Comparisons with standard models using post-hoc concept analysis features like linear probes indicate that these models fall short in accurately capturing the alignment needed for effective intervention. Concept bottleneck models show higher concept accuracy when evaluated with such probes, underscoring the inherent advantage of integrating concepts structurally within the model rather than as an interpretative overlay.
4. Robustness to Shifts: These bottleneck models also show enhanced robustness to covariate shifts. For instance, in scenarios like the TravelingBirds dataset, where background associations change significantly between training and test datasets, bottleneck models maintain performance better than standard models. This robustness comes from their reliance on more stable, concept-level representations rather than potentially transient and spurious input features.

Practical and Theoretical Implications

The practical significance of concept bottleneck models is evident across domains where interpretability and interactive refinement are crucial. These models provide a robust framework for engagements in fields requiring high transparency, allowing for the construction of systems that are both accurate and user-accessible. Theoretical implications suggest a potential paradigm shift toward models that inherently understand and utilize human-specified concepts as stepping stones to predictions.

Future Directions

Given the promising results, several future directions are plausible. Firstly, exploring methods to automatically extract or refine concepts can provide broader applicability and reduce the upfront requirement for concept annotations. Expanding the framework to manage concepts with incomplete annotations and building models resilient to a wider range of distribution shifts remains a compelling area for further research.

In conclusion, concept bottleneck models offer a balanced approach between predictive accuracy and interpretability, enabling models to function as collaborative tools that bridge the complexity of machine learning with the practical knowledge of human users. The paper lays a substantial groundwork for future innovations that could transform how AI systems integrate with expert domains.