Circuit Representations of Random Forests with Applications to XAI

TL;DR

This paper introduces a method to compile random forest classifiers into circuits for efficient explanation generation, robustness analysis, and decision flipping, advancing explainable AI techniques.

Contribution

It presents a novel circuit compilation approach for random forests, enabling efficient computation of explanations and robustness measures.

Findings

Our approach is significantly more efficient than existing methods.

We can compute complete reasons and explanations for decisions.

The method effectively identifies shortest decision-flip paths.

Abstract

We make three contributions in this paper. First, we present an approach for compiling a random forest classifier into a set of circuits, where each circuit directly encodes the instances in some class of the classifier. We show empirically that our proposed approach is significantly more efficient than existing similar approaches. Next, we utilize this approach to further obtain circuits that are tractable for computing the complete and general reasons of a decision, which are instance abstractions that play a fundamental role in computing explanations. Finally, we propose algorithms for computing the robustness of a decision and all shortest ways to flip it. We illustrate the utility of our contributions by using them to enumerate all sufficient reasons, necessary reasons and contrastive explanations of decisions; to compute the robustness of decisions; and to identify all shortest ways to flip the decisions made by random forest classifiers learned from a wide range of datasets.