Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems

IEEE Transactions on Evolutionary Computation, vol. 29, pp. 2213–2228

Abstract

Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address the need for human-understandable AI systems. Evolutionary computation (EC), a family of powerful optimization and learning algorithms, offers significant potential to contribute to XAI, and vice versa. This article provides an introduction to XAI and reviews current techniques for explaining machine learning (ML) models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques. Furthermore, we discuss the application of XAI principles within EC itself, investigating how these principles can illuminate the behavior and outcomes of EC algorithms, their (automatic) configuration, and the underlying problem landscapes they optimize. Finally, we discuss open challenges in XAI and highlight opportunities for future research at the intersection of XAI and EC. Our goal is to demonstrate EC’s suitability for addressing current explainability challenges and to encourage further exploration of these methods, ultimately contributing to the development of more understandable and trustworthy ML models and EC algorithms.

Authors 10

1. Ryan Zhou

   Queen's University

   Affiliation as printed

   School of Computing, Queen’s University, Kingston, ON, Canada

2. Jaume Bacardit

   Newcastle University

   Affiliation as printed

   School of Computing Science, Newcastle University, Newcastle upon Tyne, U.K

3. Alexander E. I. Brownlee

   University of Stirling

   Affiliation as printed

   Department of Computing Science and Mathematics, University of Stirling, Stirling, U.K

4. Stefano Cagnoni

   University of Parma

   Affiliation as printed

   Dipartimento di Ingegneria e Architettura, University of Parma, Parma, Italy

5. Martin Fyvie

   Robert Gordon University

   Affiliation as printed

   National Subsea Centre, Robert Gordon University, Aberdeen, U.K

6. Giovanni Iacca

   University of Trento

   Affiliation as printed

   Department of Information Engineering and Computer Science, Universita degli Studi di Trento, Trento, Italy

7. John McCall

   Robert Gordon University

   Affiliation as printed

   IDEAS Research Institute, Robert Gordon University, Aberdeen, U.K

8. Bas van Stein Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Universiteit Leiden, Leiden, The Netherlands

9. David J. Walker

   University of Exeter

   Affiliation as printed

   Department of Computer Science, University of Exeter, Exeter, U.K

10. Ting Hu

    Queen's University

    Affiliation as printed

    School of Computing, Queen’s University, Kingston, ON, Canada