Declarative Approaches to Counterfactual Explanations for Classification

TL;DR

This paper introduces declarative answer-set programming methods for generating counterfactual explanations in classification tasks, capable of handling black-box and rule-based models, and incorporating domain knowledge.

Contribution

It presents a novel logic programming framework for computing counterfactuals and responsibility scores, extending to probabilistic and domain-aware scenarios.

Findings

Applicable to black-box and rule-based models

Enables computation of maximum responsibility scores

Incorporates semantic and probabilistic extensions

Abstract

We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely "responsibility scores". The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this work is on the specification and computation of "best" counterfactual entities, i.e. those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown.