Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (1703.03717v2)

Abstract: Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test.

• The paper demonstrates that penalizing non-relevant input gradients directs models to learn from the right reasons.
• It incorporates domain knowledge to guide the training process, aligning model explanations with valid decision boundaries.
• Empirical results across multiple datasets show that constrained explanations improve accuracy and reduce reliance on misleading features.

Overview of "Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations"

The paper "Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations" offers a method to improve the trustworthiness and generalizability of neural networks by focusing on the quality of their explanations. While neural networks excel in many supervised learning tasks, their opacity can limit their applicability, especially in scenarios where training and testing conditions differ. This paper addresses such concerns by introducing a method that constrains the explanations of model predictions to align with domain knowledge, thereby ensuring that models not only yield accurate predictions but do so for the right reasons.

Contributions and Methodology

The paper's primary contributions can be summarized as follows:

1. Validation of Input Gradient Explanations: The authors demonstrate that input gradient explanations align with well-established methods like LIME, providing a reliable measure of a model’s decision boundaries.
2. Optimization with Domain Knowledge: By incorporating annotations that highlight irrelevant input features, the method optimizes models to seek alternative, valid explanations, effectively reducing reliance on misleading features.
3. Unsupervised Discovery of Decision Boundaries: In scenarios lacking annotations, the method iteratively discovers diverse decision boundaries, allowing experts to review a range of models for validity.

Utilizing input gradients, the authors propose penalizing non-relevant input features in a model’s loss function. This penalty effectively biases the learning process away from these distractions, thus enhancing the model's generalization capabilities.

Empirical Results

The paper showcases the efficacy of their approach through experiments on several datasets, including a toy color dataset, 20 Newsgroups, Iris-Cancer, and Decoy MNIST. Here are some highlights:

• Toy Color Dataset: Demonstrates that models can be trained to learn correct rules through constrained gradients, which showcases the method’s ability to overcome misleading decision patterns.
• 20 Newsgroups and Real-World Datasets: Experiments revealed that input gradient methods perform comparably to LIME, while being computationally efficient.
• Generalization: On complex datasets like Decoy MNIST, explanation constraints improved test accuracy markedly by guiding models away from spurious correlations.

Implications and Future Directions

The approach outlined in this paper holds practical implications for improving model interpretability and robustness, particularly in domains where model decisions need scrutiny. The proposed methodology may avert potentially harmful decisions in sensitive applications, such as healthcare, by ensuring that models adhere to domain-relevant explanations.

Theoretically, this suggests a pathway toward integrating human-in-the-loop oversight for model training, where human experts iteratively refine model explanations. The concept of ensuring models are right for the “right reasons” offers a promising avenue for addressing issues of fairness, accountability, and robustness in machine learning systems.

Conclusion

Overall, the paper brings forward an important dialogue about the intersection of model accuracy and explanatory trustworthiness. By leveraging input gradients, the researchers provide a scalable technique to ensure models perform robustly across varying conditions. Future research should further explore the balance between computational efficiency and interpretability, as well as extensions to diverse machine learning architectures beyond neural networks.