# Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems

**Authors:** Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu  
**arXiv:** [2406.07811](https://arxiv.org/abs/2406.07811)  
**Date:** 2024-06-12  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2406.07811v1/

## Key points

- AI methods are increasingly prevalent across various applications
- Concerns exist about the black-box nature of AI models, hindering accountability and trust
- Explainable artificial intelligence (XAI) aims to provide human-understandable explanations for AI model outputs
- Integration of evolutionary computation (EC) techniques within XAI can enhance transparency and interpretability in AI systems
- The paper explores XAI principles, reviews current techniques for explaining ML models, and highlights approaches incorporating EC methodologies
- Application of XAI principles within EC elucidates behavior and outcomes of EC algorithms, enhancing explainability and trustworthiness of ML models and EC algorithms
- Open challenges in XAI are addressed alongside opportunities for future research leveraging EC methodologies

## Summary

In the rapidly evolving landscape of artificial intelligence (AI), the use of AI methods has become increasingly prevalent across various applications. However, concerns have been raised regarding the inherent black-box nature of these AI models, which can hinder accountability and trust in their decision-making processes. In response to this challenge, the field of explainable artificial intelligence (XAI) has emerged, aiming to provide human-understandable explanations for AI model outputs. One promising avenue within XAI is the integration of evolutionary computation (EC) techniques. EC, as a versatile family of optimization and learning tools, holds significant potential to enhance transparency and interpretability in AI systems. This paper delves into an exploration of XAI principles and reviews current techniques used for explaining machine learning (ML) models. It then delves into how EC can be effectively leveraged within XAI frameworks, highlighting approaches that incorporate EC methodologies. Furthermore, the paper discusses the application of XAI principles within EC itself, shedding light on how these principles can elucidate the behavior and outcomes of EC algorithms. By examining automatic configuration processes and underlying problem landscapes optimized by these algorithms, insights are gained into enhancing the explainability and trustworthiness of both ML models and EC algorithms. As the discussion unfolds, open challenges in XAI are addressed alongside opportunities for future research leveraging EC methodologies. The authors aim to showcase that EC is well-suited to tackle existing issues surrounding explainability in AI systems while encouraging further exploration to foster the development of more transparent and trustworthy ML models and EC algorithms. Through this roadmap towards transparent intelligent systems, a pathway is paved for advancing XAI with robust foundations rooted in evolutionary computation principles.