Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors (2404.03713v2)

Abstract: Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. 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: (1) inconsistency across layers, (2) entanglement with other concepts, and (3) spatial dependency. 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 each property can lead to misleading explanations, and provide recommendations to mitigate their impact. To demonstrate practical applications, we apply our recommendations to a melanoma classification task, showing how entanglement can lead to uninterpretable results and that the choice of negative probe set can have a substantial impact on the meaning of a CAV. Further, we show that 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 natural images (ImageNet), skin lesions (ISIC 2019), 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.

• The paper demonstrates that Concept Activation Vectors vary across neural network layers, exposing inconsistency in model interpretation.
• It introduces the 'Elements' synthetic dataset to control and explore relationships between concepts and classes.
• The study provides actionable insights to improve deep model transparency and advance explainable AI through nuanced analysis.

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.