PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

Researchers have introduced PACE, a new framework designed to improve how machine learning models explain their decisions through counterfactual examples. Unlike existing methods that often suggest unrealistic changes to input data, this approach incorporates symbolic constraints to ensure that the suggested alternatives remain plausible and actionable for users.