Topological Interpretability for Deep-Learning (2305.08642v2)

Abstract: With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

An Analysis of Topological Interpretability in Deep Learning

The paper "Topological Interpretability for Deep-Learning" offers an innovative approach to understanding deep learning (DL) models through the lens of topological data analysis (TDA). This work addresses the critical issue of interpretability in AI, particularly in high-stakes domains such as healthcare and criminal justice. The authors ambitiously propose a methodology that leverages topological and geometric data analysis to infer significant features in deep learning models, thereby providing insights into the decision-making process of these models.

Methodological Overview

The core of the paper's methodology revolves around the creation of a Mapper graph—a topological technique used to construct a low-dimensional representation of high-dimensional data. This is achieved by partitioning the input space using a filter function and clustering similar data points. By employing ground truth labels as one of the filter criteria, the methodology ensures that the clusters are homogeneous regarding the true class. This innovative approach significantly contributes to understanding which features influence DL model predictions and ensures these are grounded in the original data set.

A distinctive aspect of this work is its use of the distance to measure (dtm) function for assessing the proximity of individual features to probability measures associated with high predictive accuracy. The dtm is less sensitive to noise, hence offering a robust mechanism to identify relevant features for specific classifications while mitigating the influence of outliers.

Results and Implications

The paper presents results from two datasets: cancer pathology reports and the 20 Newsgroups dataset. In both cases, the Mapper graph effectively clustered text features, revealing insightful patterns. For instance, the analysis of primary cancer sites in the pathology reports successfully identified clinically relevant keywords, aligning well with known medical literature. Similarly, distinguishing features in the 20 Newsgroups dataset effectively reflected semantic differences among various topics.

The implications of these findings are multifaceted. Practically, this interpretability framework could improve trust in AI models used in high-risk domains by elucidating the basis of their decisions. Theoretically, it underscores the feasibility of employing topology as a scaffold for model interpretability, potentially paving the path for more robust and transparent AI systems.

Discussion of Limitations and Future Directions

The paper candidly discusses potential limitations, notably the challenge of considering words in context within text data, which could be mitigated by extending the approach to handle word n-grams. Future work could investigate this aspect, potentially enhancing the interpretability of models dealing with natural language processing tasks.

Moreover, the paper emphasizes the stability of their method, comparing it favorably with LIME and SHAP in terms of Lipschitz stability. However, computational complexity remains a consideration, especially in the Mapper algorithm's Hausdorff distance computations and k-nearest neighbor searches. Future work could focus on optimizing these aspects to further streamline the interpretability process.

Conclusion

This paper significantly contributes to the field by introducing a topologically-based framework for deep learning interpretability. Its methodology not only showcases the utility of TDA in AI but also enriches the dialogue around interpretability in machine learning. By doing so, it lays the groundwork for future explorations that could integrate topological insights with traditional interpretability methods, potentially enhancing the trustworthiness and reliability of AI implementations across critical domains.