Evolutionary approaches to explainable machine learning

TL;DR

This paper reviews how evolutionary computing techniques can enhance explainability in machine learning models, addressing current challenges and proposing future research directions for more transparent AI systems.

Contribution

It provides an overview of XAI/XML, reviews existing methods incorporating evolutionary computing, and discusses open challenges and opportunities for future research in this area.

Findings

Evolutionary computing shows promise in improving model explainability.

Current EC-based XAI methods address key transparency challenges.

Future research can further integrate EC techniques for trustworthy AI.

Abstract

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.