可解释机器学习的进化方法
Evolutionary approaches to explainable machine learning
AI总结：
本文综述进化计算在可解释机器学习中的应用，旨在通过进化技术提升模型透明度和可信度，并展望未来研究方向。
摘要：
机器学习模型正越来越多地被应用于关键领域，但其黑箱特性引发了对问责制和信任的担忧。可解释人工智能（XAI）或可解释机器学习（XML）领域应运而生，以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和学习工具，在推动XAI/XML方面具有巨大潜力。在本章中，我们简要介绍XAI/XML，并回顾当前用于解释机器学习模型的各种技术。随后，我们重点探讨进化计算如何应用于XAI/XML，并综述一些融合了进化计算技术的方法。我们还讨论了XAI/XML中存在的开放性挑战以及利用进化计算在该领域开展未来研究的机遇。我们的目标是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，以促进开发更加透明、可信和可问责的机器学习模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
