# Explaining Deep Learning Models with Constrained Adversarial Examples

## TL;DR

This paper introduces CADEX, a method for generating constrained adversarial examples that provide actionable, domain-aware explanations for model decisions, improving interpretability in real-world applications.

## Contribution

It proposes a novel approach for generating counterfactual explanations that respect domain constraints, enhancing interpretability of deep learning models.

## Key findings

- CADEX produces explanations that incorporate domain constraints.
- The method is applicable to real-world scenarios.
- It improves understanding of model decisions with actionable insights.

## Abstract

Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decision to be made, and to give an informative explanation. We explore a new method of generating counterfactual explanations, which instead of explaining why a particular classification was made explain how a different outcome can be achieved. This gives the recipients of the explanation a better way to understand the outcome, and provides an actionable suggestion. We show that the introduced method of Constrained Adversarial Examples (CADEX) can be used in real world applications, and yields explanations which incorporate business or domain constraints such as handling categorical attributes and range constraints.