Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

TL;DR

This paper introduces a feature-based learning framework for generating diverse, actionable, and privacy-preserving counterfactual explanations in machine learning, addressing computational challenges and privacy concerns.

Contribution

It presents a novel, flexible method that efficiently produces diverse counterfactuals while ensuring privacy, advancing explainability in black-box models.

Findings

More efficient than comparable methods

Generates diverse and plausible counterfactuals

Achieves lower re-identification risks

Abstract

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks.