ACE: Adapting sampling for Counterfactual Explanations
This work addresses the practical challenge of costly and limited model access in counterfactual explanation generation, offering a more efficient solution for interpretability in classification tasks.
The paper tackles the problem of sample inefficiency in generating counterfactual explanations for machine learning models, proposing the ACE algorithm that reduces the number of model queries needed while maintaining accuracy, achieving superior evaluation efficiency compared to state-of-the-art methods.
Counterfactual Explanations (CFEs) interpret machine learning models by identifying the smallest change to input features needed to change the model's prediction to a desired output. For classification tasks, CFEs determine how close a given sample is to the decision boundary of a trained classifier. Existing methods are often sample-inefficient, requiring numerous evaluations of a black-box model -- an approach that is both costly and impractical when access to the model is limited. We propose Adaptive sampling for Counterfactual Explanations (ACE), a sample-efficient algorithm combining Bayesian estimation and stochastic optimization to approximate the decision boundary with fewer queries. By prioritizing informative points, ACE minimizes evaluations while generating accurate and feasible CFEs. Extensive empirical results show that ACE achieves superior evaluation efficiency compared to state-of-the-art methods, while maintaining effectiveness in identifying minimal and actionable changes.