Explaining Explainability: Understanding Concept Activation Vectors (2404.03713v1)

Abstract: Recent interpretability methods propose using concept-based explanations to translate the internal representations of deep learning models into a language that humans are familiar with: concepts. This requires understanding which concepts are present in the representation space of a neural network. One popular method for finding concepts is Concept Activation Vectors (CAVs), which are learnt using a probe dataset of concept exemplars. In this work, we investigate three properties of CAVs. CAVs may be: (1) inconsistent between layers, (2) entangled with different concepts, and (3) spatially dependent. Each property provides both challenges and opportunities in interpreting models. We introduce tools designed to detect the presence of these properties, provide insight into how they affect the derived explanations, and provide recommendations to minimise their impact. Understanding these properties can be used to our advantage. For example, we introduce spatially dependent CAVs to test if a model is translation invariant with respect to a specific concept and class. Our experiments are performed on ImageNet and a new synthetic dataset, Elements. Elements is designed to capture a known ground truth relationship between concepts and classes. We release this dataset to facilitate further research in understanding and evaluating interpretability methods.

Exploring the Intricacies of Concept Activation Vectors in Model Interpretability

Introduction

The transparency and interpretability of deep learning models, particularly those in critical domains, have been subjects of increasing research focus. Concept Activation Vectors (CAVs) present an innovative approach to interpreting these models by mapping high-dimensional data into interpretable, human-understandable concepts. This paper examines three critical properties of CAVs: inconsistency across layers, entanglement with different concepts, and spatial dependence. Through a detailed investigation and the introduction of a novel synthetic dataset, "Elements," this paper offers insights into the advantages and limitations of using CAVs for model interpretation.

Exploring CAVs: Theoretical Insights and Practical Tools

Inconsistency Across Layers

The paper underlines that CAV representations may vary significantly across different layers of a neural network. This inconsistency can lead to varying interpretations of the same concept when analyzed at different depths of the model. Tools for detecting such inconsistencies are introduced, facilitating a more nuanced understanding of how concepts evolve across layers.

Concept Entanglement

Another property scrutinized is the potential entanglement of CAVs with multiple concepts. This entanglement challenges the assumption that CAVs represent a single, isolated concept. The paper provides visualization tools to detect and understand the extent of concept entanglement within models, thereby refining the interpretability of CAV-based explanations.

Spatial Dependence

CAVs' spatial dependence is meticulously investigated, revealing that CAVs could encode the location-specific information of concepts in the input space. The introduction of spatially dependent CAVs represents a significant advancement, enabling the exploration of models' translation invariance concerning specific concepts and classes.

Elements: A Configurable Synthetic Dataset

One of the paper's notable contributions is the creation of the "Elements" dataset. Elements is designed with the flexibility to manipulate the relationship between concepts and classes, supporting the investigation of interpretability methods. This dataset allows for the controlled paper of model behavior and the implications of concept vector properties, thereby providing a valuable resource for future interpretability research.

Implications and Future Research Directions

The insights garnered from investigating the consistency, entanglement, and spatial dependence of CAVs carry profound implications for the field of explainable AI. They illuminate the complexities inherent in interpreting deep learning models and underscore the importance of nuanced, layered analysis.

Extending beyond the scope of CAV-based explanations, this research paves the way for exploring alternative concept representations and their interpretability potential. Moreover, the Elements dataset stands as a cornerstone for further endeavors aiming to dissect and enhance model transparency.

Conclusion

In conclusion, this examination of CAV properties through analytical and empirical lenses unravels complexities that are crucial for advancing model interpretability. By addressing the challenges posed by inconsistency, entanglement, and spatial dependence of CAVs, and by introducing the Elements dataset, the research contributes significantly to the nuanced understanding and application of concept-based explanations in AI.