Hierarchical interpretations for neural network predictions (1806.05337v2)

Abstract: Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. Using examples from Stanford Sentiment Treebank and ImageNet, we show that ACD is effective at diagnosing incorrect predictions and identifying dataset bias. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.

• The paper introduces Agglomerative Contextual Decomposition (ACD) to hierarchically decompose feature interactions for explaining DNN predictions.
• The methodology generalizes contextual decomposition to various architectures, including CNNs and LSTMs, enabling multi-level analysis of non-linear contributions.
• Empirical evaluations demonstrate that ACD improves model trust by offering robust and stable interpretations, even under adversarial perturbations.

A Formal Review of "Hierarchical Interpretations for Neural Network Predictions"

In the domain of deep learning, interpretability remains a critical yet challenging aspect that influences the practical deployment of neural networks in diverse fields such as healthcare, public policy, and science. The paper "Hierarchical Interpretations for Neural Network Predictions" by Singh, Murdoch, and Yu addresses the issue of interpretability by proposing a novel method called Agglomerative Contextual Decomposition (ACD). This method provides a hierarchical explanation of deep neural network (DNN) predictions and aims to make these models more transparent and trustworthy.

Methodological Overview

Agglomerative Contextual Decomposition (ACD)

The authors develop ACD as an extension of the contextual decomposition methodology, which was initially applied to LSTMs for extracting importance scores. ACD generalizes this framework for broader application across various DNN architectures, including CNNs. The fundamental contribution is its hierarchical clustering mechanism, which efficiently identifies and visualizes feature interaction groups that significantly contribute to a DNN’s prediction. This approach leverages a form of agglomerative hierarchical clustering that uses the calculated importance scores as a joining metric.

Key Contributions

1. Generalization of Contextual Decomposition: The paper successfully expands the applicability of contextual decomposition by supporting its execution across different network types. This involves introducing a layer-wise decomposition strategy compatible with standard operations found in CNNs.
2. Hierarchical Interpretation: The hierarchical nature of ACD enables it to illustrate feature importance at multiple levels, ranging from individual elements to groups, thus making non-linear feature interactions more explicit for users.
3. Robustness to Adversarial Perturbations: ACD exhibits strong resilience to adversarial inputs, suggesting that the hierarchical features identified are stable and represent fundamental aspects of the data rather than artifacts.

Empirical Evaluation

The methodology is validated through both qualitative and quantitative experiments. By employing ACD on several datasets—SST, MNIST, and ImageNet—the authors demonstrate its effectiveness in producing intuitive and stable visualizations:

• Qualitative Insights: ACD effectively aids in diagnosing DNN predictions by indicating potential biases or erroneous interactions. For instance, text examples reveal how sentiment predictions are influenced by compound phrases.
• Quantitative Human Studies: Surveys conducted among graduate students show that ACD facilitates better model trust and helps users discern the more accurate model among alternatives. Particularly, it outperforms previous non-hierarchical techniques in complex settings such as ImageNet classification.

Theoretical and Practical Implications

ACD marks a significant step forward in interpretability research by providing a method that aggregates saliency information hierarchically, effectively bridging local and global interpretability methods. This capability is crucial for several reasons:

• Model Debugging and Trust: Understanding feature interactions allows users to identify model weaknesses and biases, crucial for sectors where decision-making impact is high.
• Generalization Across Models: By extending the methodology beyond specific architectures, ACD presents a more universal solution that can adapt to rapidly evolving DNN technologies.
• Robustness to Adversarial Attacks: The indication that ACD’s hierarchical explanation is robust implies potential applications in improving model defenses against adversarial attacks, which remain a prominent concern for deploying AI systems.

Future Prospects

The hierarchical nature and model-agnostic potential of ACD suggest several avenues for future work. Enhancements could focus on optimizing the computational efficiency of the methodology, particularly for real-time applications and large-scale models. Additionally, exploring the integration of ACD with dynamic network architectures like transformers could yield insights and improvements in more complex tasks and evolving deep learning paradigms.

To conclude, the introduction of ACD provides a substantive foundation for enhancing the interpretability of deep neural networks. By offering hierarchical interpretations, this approach empowers users to gain deeper insights into model behavior, paving the way for more transparent and accountable AI systems.