Machine learning models are increasingly being used in critical sectors, but their black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) or explainable machine learning (XML) has emerged in response to the need for human understanding of these models. Evolutionary computing, as a family of powerful optimization and learning tools, has significant potential to contribute to XAI/XML. In this chapter, we provide a brief introduction to XAI/XML and review various techniques in current use for explaining machine learning models. We then focus on how evolutionary computing can be used in XAI/XML, and review some approaches which incorporate EC techniques. We also discuss some open challenges in XAI/XML and opportunities for future research in this field using EC. Our aim is to demonstrate that evolutionary computing 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, trustworthy and accountable machine learning models.

翻译：机器学习模型正越来越多地应用于关键领域，但其黑箱特性引发了人们对问责性和信任度的担忧。可解释人工智能（XAI）或可解释机器学习（XML）领域应运而生，旨在满足人类理解这些模型的需求。进化计算作为一类强大的优化与学习工具，在XAI/XML方面具有显著潜力。本章首先简要介绍XAI/XML，并综述当前用于解释机器学习模型的各种技术，随后重点探讨进化计算在XAI/XML中的应用，回顾一些融入进化计算技术的方法。我们还讨论了XAI/XML领域中的若干开放性挑战，以及利用进化计算开展未来研究的机遇。本研究旨在证明进化计算非常适合解决当前可解释性问题，并鼓励进一步探索这些方法，以推动开发更透明、可信且可问责的机器学习模型。