# Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

## TL;DR

This paper introduces a framework for generating diverse, feasible, and actionable counterfactual explanations for machine learning classifiers, improving interpretability and decision boundary understanding.

## Contribution

It proposes a novel method using determinantal point processes to generate diverse counterfactuals and provides metrics for evaluating their actionability and diversity.

## Key findings

- Outperforms prior methods in generating diverse counterfactuals
- Effectively approximates local decision boundaries
- Framework is applicable to real-world datasets

## Abstract

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations should satisfy two properties: feasibility of the counterfactual actions given user context and constraints, and diversity among the counterfactuals presented. To this end, we propose a framework for generating and evaluating a diverse set of counterfactual explanations based on determinantal point processes. To evaluate the actionability of counterfactuals, we provide metrics that enable comparison of counterfactual-based methods to other local explanation methods. We further address necessary tradeoffs and point to causal implications in optimizing for counterfactuals. Our experiments on four real-world datasets show that our framework can generate a set of counterfactuals that are diverse and well approximate local decision boundaries, outperforming prior approaches to generating diverse counterfactuals. We provide an implementation of the framework at https://github.com/microsoft/DiCE.