A Symbolic Approach for Counterfactual Explanations

TL;DR

This paper introduces a symbolic method for generating counterfactual explanations by encoding classifier decision functions as CNF formulas and identifying minimal correction subsets, demonstrated on Bayesian classifiers.

Contribution

It presents a novel symbolic approach that encodes classifiers as CNF formulas to efficiently generate counterfactual explanations using minimal correction subsets.

Findings

Potential of the approach demonstrated on Bayesian classifiers

Effective in identifying features to change for prediction alteration

Leverages existing solutions for minimal correction subset generation

Abstract

In this paper titled A Symbolic Approach for Counterfactual Explanations we propose a novel symbolic approach to provide counterfactual explanations for a classifier predictions. Contrary to most explanation approaches where the goal is to understand which and to what extent parts of the data helped to give a prediction, counterfactual explanations indicate which features must be changed in the data in order to change this classifier prediction. Our approach is symbolic in the sense that it is based on encoding the decision function of a classifier in an equivalent CNF formula. In this approach, counterfactual explanations are seen as the Minimal Correction Subsets (MCS), a well-known concept in knowledge base reparation. Hence, this approach takes advantage of the strengths of already existing and proven solutions for the generation of MCS. Our preliminary experimental studies on Bayesian classifiers show the potential of this approach on several datasets.