Evolutionary approaches to explainable machine learning
Ryan Zhou Ting Hu
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.