On Completeness-aware Concept-Based Explanations in Deep Neural Networks (1910.07969v6)

Abstract: Human explanations of high-level decisions are often expressed in terms of key concepts the decisions are based on. In this paper, we study such concept-based explainability for Deep Neural Networks (DNNs). First, we define the notion of completeness, which quantifies how sufficient a particular set of concepts is in explaining a model's prediction behavior based on the assumption that complete concept scores are sufficient statistics of the model prediction. Next, we propose a concept discovery method that aims to infer a complete set of concepts that are additionally encouraged to be interpretable, which addresses the limitations of existing methods on concept explanations. To define an importance score for each discovered concept, we adapt game-theoretic notions to aggregate over sets and propose ConceptSHAP. Via proposed metrics and user studies, on a synthetic dataset with apriori-known concept explanations, as well as on real-world image and language datasets, we validate the effectiveness of our method in finding concepts that are both complete in explaining the decisions and interpretable. (The code is released at https://github.com/chihkuanyeh/concept_exp)

Overview of Completeness-Aware Concept-Based Explanations in Deep Neural Networks

This paper introduces a novel approach for concept-based explainability within deep neural networks (DNNs), focused on developing a framework to evaluate and improve the interpretability of model decisions through high-level, human-understandable concepts. The authors tackle the challenge of making DNNs more transparent by introducing a measure called "completeness," which assesses how sufficiently a set of discovered concepts can explain the model's prediction behavior.

The authors propose that explanations based on raw input features can be less interpretable for humans, especially for complex models that utilize lower-level information such as pixels or word embeddings. To address this issue, they leverage the notion of "concept-based thinking," where similar instances are grouped to form systematic explanations understandable to humans. Thus, their work emphasizes developing explanations that align more closely with how humans naturally comprehend and communicate decision-making processes.

Key Contributions

1. Completeness Metric: A novel metric is introduced to quantify the completeness of a set of concepts in explaining a model’s behavior. The completeness metric is rooted in evaluating whether the concepts are sufficient statistics for the model predictions. In other words, complete concepts should enable accurate predictions when the model acts solely based on their scores.
2. Concept Discovery Algorithm: An unsupervised algorithm is developed to discover complete and interpretable concepts. The algorithm operates under the assumption that concepts lie in low-dimensional subspaces of some intermediate DNN activations. It is coupled with an interpretability regularizer to ensure the semantic understanding and distinctiveness of discovered concepts.
3. ConceptSHAP: Inspired by game-theoretic principles, ConceptSHAP is proposed to assign importance scores to individual concepts. This enables the quantification of each concept’s contribution to the overall completeness score, providing insight into which concepts are crucial for particular model predictions.
4. Comprehensive Evaluation: The approach is validated through synthetic data where ground truth concepts are known, as well as real-world image and language datasets. The evaluations highlight the method's ability to discover coherent and complete explanations that align with human understanding.

Implications and Future Directions

The implications of this research are profound for the development of more interpretable AI systems. The framework not only advances the field of model interpretability but also provides concrete metrics and methodologies to assess and enhance the transparency of complex neural networks. These contributions are particularly significant in domains where model trust and accountability are critical, such as healthcare and transportation.

Looking ahead, one potential direction for future research is the integration of completeness-aware concept-based explanations within the training process of neural networks to create inherently interpretable models. This might involve collaborative training approaches where both interpretability and predictive performance objectives are jointly optimized. Another avenue for exploration is extending the framework to other data modalities, such as audio or video, to generalize the applicability of concept-based explanations beyond images and text.

In summary, the paper sets a strong precedent for combining technical rigor with interpretability objectives in deep learning, promoting the advancement of AI systems that are not only effective but also comprehensible to human stakeholders.