Optimal counterfactual explanations for k-Nearest Neighbors using Mathematical Optimization and Constraint Programming

\(\) Within the topic of explainable AI, counterfactual explanations to classifiers have received significant recent attention. We study counterfactual explanations that try to explain why a data point received an undesirable classification by providing the closest data point that would have received a desirable one. Within the context of one the simplest and most popular … Read more