Synthesizing Pareto-Optimal Interpretations for Black-Box Models

TL;DR

This paper introduces a multi-objective optimization framework to generate a diverse set of Pareto-optimal interpretations for black-box models, balancing correctness and explainability, and demonstrating its effectiveness on neural network classifiers.

Contribution

It presents a general multi-objective synthesis method for interpretations, allowing flexible trade-offs and using constraint solving, which surpasses single-objective approaches.

Findings

Multiple Pareto-optimal interpretations exist for black-box models.

The approach can synthesize diverse interpretations missed by existing methods.

Application to neural networks shows richer interpretation options.

Abstract

We present a new multi-objective optimization approach for synthesizing interpretations that "explain" the behavior of black-box machine learning models. Constructing human-understandable interpretations for black-box models often requires balancing conflicting objectives. A simple interpretation may be easier to understand for humans while being less precise in its predictions vis-a-vis a complex interpretation. Existing methods for synthesizing interpretations use a single objective function and are often optimized for a single class of interpretations. In contrast, we provide a more general and multi-objective synthesis framework that allows users to choose (1) the class of syntactic templates from which an interpretation should be synthesized, and (2) quantitative measures on both the correctness and explainability of an interpretation. For a given black-box, our approach yields a set of Pareto-optimal interpretations with respect to the correctness and explainability measures. We show that the underlying multi-objective optimization problem can be solved via a reduction to quantitative constraint solving, such as weighted maximum satisfiability. To demonstrate the benefits of our approach, we have applied it to synthesize interpretations for black-box neural-network classifiers. Our experiments show that there often exists a rich and varied set of choices for interpretations that are missed by existing approaches.