Interpretable & Explorable Approximations of Black Box Models (1707.01154v1)

Abstract: We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity to the original model and interpretability of the explanation. To this end, we develop a novel objective function which allows us to learn (with optimality guarantees), a small number of compact decision sets each of which explains the behavior of the black box model in unambiguous, well-defined regions of feature space. Furthermore, our framework also is capable of accepting user input when generating these approximations, thus allowing users to interactively explore how the black-box model behaves in different subspaces that are of interest to the user. To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with real-world datasets and user studies demonstrates that our approach can generate highly compact, easy-to-understand, yet accurate approximations of various kinds of predictive models compared to state-of-the-art baselines.

Interpretable Content Explorable Approximations of Black Box Models

The paper “Interpretable Content Explorable Approximations of Black Box Models” presents the development and evaluation of Black Box Explanations through Transparent Approximations (BETA), a framework designed to create interpretable approximations for complex machine learning models. As machine learning algorithms become more integral in critical decision-making areas such as healthcare and criminal justice, the necessity for interpretability, clarity, and trust in model predictions becomes paramount. This paper addresses the challenge of generating global explanations for any black-box model, balancing intricacies like interpretability, fidelity, and unambiguity with a user-interactive component.

BETA Framework Overview

The BETA framework emphasizes creating compact decision sets to provide interpretable approximations of black box models. It introduces a new objective function which optimally balances model fidelity (how closely the approximation matches the original model), interpretability (ease of understanding by humans), and unambiguity (distinct decision rationale within the feature space). The framework is model-agnostic and allows user interaction, enabling stakeholders to explore model behavior in specified subspaces of interest.

Technical Contribution

The technical contribution of the paper includes the formulation of a novel optimization problem that captures the aforementioned desiderata effectively. The authors have shown that while the problem is NP-hard, it aligns with a non-normal, non-monotone submodular setup with matroid constraints, allowing approximation solutions with theoretically backed efficiency.

Furthermore, the paper introduces a two-level decision set representation. This representation strikes a balance between expressiveness and simplicity, incorporating nested if-then rules which are split into neighborhood descriptors and decision logic rules. This separation aids in maintaining approximation simplicity while capturing the essential informational depth needed to explain the model's operations within specific feature regions.

Experimental Evaluation

The authors conducted comprehensive experimental evaluations using real-world datasets, including a depression diagnosis dataset. Their experiments demonstrate the superiority of BETA over baseline models such as LIME, IDS, and BDL, especially in achieving high agreement rates with black box models at minimal interpretability costs. The reported results show that BETA's approximations consistently outperform others in terms of maintaining fidelity at lower complexities.

User Studies

Adding a qualitative dimension, the paper includes user studies assessing human comprehension using BETA's approximations. Participants were tasked to reason about neural model behaviors using rule-based explanations. Findings from these studies suggest that BETA not only accelerates user understanding but also improves accuracy in making inferences about the model's decision logic. BETA’s interaction capability further demonstrated an enhancement in user comprehension and reduced the time needed to process explanatory content.

Implications and Future Directions

The implications of the research are significant for fields requiring transparent predictive models. In particular, it paves the way for the adoption of more interpretable AI models in domains where decision transparency and accountability are critical. By incorporating user interactivity, the framework also opens up avenues for customizing model exploration based on user preferences, thereby enhancing trust and acceptance of AI systems in sensitive decision-making processes.

Future research could extend BETA by integrating it with real-time decision support systems, potentially functioning as an explanatory module in pipelines where end-users’ comprehension and feedback are essential. Moreover, exploring alternative representations and further scaling the optimization process could broaden its application across more complex models and datasets.

In conclusion, this paper contributes a robust framework to the interpretable AI domain, offering practical strategies for decoding black-box models. By optimizing for multiple interpretive facets and embracing user interactivity, BETA effectively bridges the gap between complex model behavior and stakeholder understanding.