Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems
<|reference_start|>Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems: AI methods are finding an increasing number of applications, but their often black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) has emerged in response to the need for human understanding of AI models. Evolutionary computation (EC), as a family of powerful optimization and learning tools, has significant potential to contribute to XAI. In this paper, we provide an introduction to XAI and review various techniques in current use for explaining machine learning (ML) models. We then focus on how EC can be used in XAI, and review some XAI approaches which incorporate EC techniques. Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize. Finally, we discuss some open challenges in XAI and opportunities for future research in this field using EC. Our aim is to demonstrate that EC is well-suited for addressing current problems in explainability and to encourage further exploration of these methods to contribute to the development of more transparent and trustworthy ML models and EC algorithms.<|reference_end|>
@article{zhou2024evolutionary,
    title={Evolutionary Computation and Explainable AI: A Roadmap to Understandable
  Intelligent Systems},
    author={Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin
  Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu},
    journal={arXiv preprint arXiv:2406.07811},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07811},
    primaryClass={cs.NE cs.AI cs.LG}
}