# Interpretable Counterfactual Explanations Guided by Prototypes

## TL;DR

This paper introduces a fast, model-agnostic method for generating interpretable counterfactual explanations using class prototypes, improving speed and interpretability for classifier predictions on image and tabular data.

## Contribution

The method leverages class prototypes to accelerate counterfactual search and introduces new metrics for local interpretability evaluation.

## Key findings

- Significantly faster counterfactual generation using prototypes
- More interpretable explanations compared to baseline methods
- Effective on both image and tabular datasets

## Abstract

We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, obtained using either an encoder or through class specific k-d trees, significantly speed up the the search for counterfactual instances and result in more interpretable explanations. We introduce two novel metrics to quantitatively evaluate local interpretability at the instance level. We use these metrics to illustrate the effectiveness of our method on an image and tabular dataset, respectively MNIST and Breast Cancer Wisconsin (Diagnostic). The method also eliminates the computational bottleneck that arises because of numerical gradient evaluation for $\textit{black box}$ models.