Interpreting Blackbox Models via Model Extraction (1705.08504v6)

Abstract: Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex, blackbox models in the form of a decision tree approximating the original model---as long as the decision tree is a good approximation, then it mirrors the computation performed by the blackbox model. We devise a novel algorithm for extracting decision tree explanations that actively samples new training points to avoid overfitting. We evaluate our algorithm on a random forest to predict diabetes risk and a learned controller for cart-pole. Compared to several baselines, our decision trees are both substantially more accurate and equally or more interpretable based on a user study. Finally, we describe several insights provided by our interpretations, including a causal issue validated by a physician.

Overview of "Interpreting Blackbox Models via Model Extraction"

The paper "Interpreting Blackbox Models via Model Extraction" by Osbert Bastani, Carolyn Kim, and Hamsa Bastani addresses a significant challenge in machine learning—understanding the behavior and rationale of complex models deployed in practical applications. The authors propose a framework for extracting interpretable decision trees from blackbox models, thereby allowing practitioners to derive meaningful insights from these opaque systems without compromising their complexity or accuracy.

Methodology

The authors introduce a novel algorithm designed to extract global interpretations of blackbox models using decision trees. Decision trees offer a structured, interpretable format despite their nonparametric nature, which facilitates robust approximation of complex models. The proposed algorithm employs active sampling to generate additional training data points, mitigating the risk of overfitting—a common issue in decision tree learning strategies. Unlike traditional methods such as rejection sampling, active sampling strategically targets portions of the data space most in need of further information, optimizing accuracy and reducing variance.

The algorithm is evaluated using two benchmarks: a random forest model for predicting diabetes risk and a learned controller for the cart-pole problem. The approach significantly improves the fidelity of extracted decision trees compared to existing baselines, achieving higher accuracy in representing the complex model while maintaining or enhancing interpretability.

Theoretical Contributions

The paper offers theoretical guarantees for the convergence of the extracted decision trees towards the optimal decision tree as the sample size increases. This claim is supported by rigorous proofs provided in the technical sections of the document, under assumptions of continuity and bounded probability distributions.

Further, the authors expand upon the conventional notions of interpretability by undertaking a user paper with machine learning experts. This assessment measures the ability of interpretable models to facilitate understanding and decision-making within critical application domains. Users perform tasks such as computing counterfactuals and identifying risky subpopulations, demonstrating that the extracted decision trees offer superior interpretability relative to competitive baselines, including rule lists and decision sets.

Numerical Results and Implications

The decision tree extraction algorithm yields strong numerical results, achieving higher fidelity than several state-of-the-art methods, including CART and born-again tree algorithms. Empirical results span multiple datasets, encompassing binary classification, regression, and reinforcement learning tasks. For instance, the approach outperforms traditional CART trees across all tested instances, indicating its robustness and versatility in diverse scenarios.

The interpretability delivered by the extracted trees has immediate implications for diagnosing potential causality issues, shifts in data distributions, and fairness biases in deployed models. The insights gleaned from this process can inform interventions or policy changes to mitigate unintended model behaviors, such as systematically overestimating risk for specific patient subpopulations.

Future Directions

Looking forward, this research opens pathways to explore more sophisticated input distributions, which could further refine the accuracy of extracted decision trees and extend their applicability to additional model families. There is also potential to enhance the visualization of decision trees, making complex models more accessible to stakeholders and facilitating the communication of complex data-driven insights.

Conclusion

The paper significantly advances the field by providing a method to translate opaque models into interpretable formats without sacrificing accuracy. This contribution is pivotal for ensuring transparent and reliable AI systems in sensitive domains like healthcare, criminal justice, and autonomous systems. As AI continues to integrate into critical infrastructures, interpretability frameworks such as the one proposed will become increasingly vital in guaranteeing ethical and effective decision-making systems.