Circuit Representations of Random Forests with Applications to XAI
This work addresses the need for interpretable AI by providing tools for explainability in random forests, though it is incremental as it builds on existing circuit-based approaches.
The paper tackles the problem of explaining random forest decisions by compiling them into circuits, which enables efficient computation of explanations and robustness measures, achieving significant efficiency gains over existing methods.
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.