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Evolutionary Approaches to Explainable Machine Learning

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Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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.

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Authors and Affiliations

  1. School of Computing, Queen’s University, Kingston, ON, K7L 2N8, Canada

    Ryan Zhou & Ting Hu

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  1. Ryan Zhou
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  2. Ting Hu
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Correspondence to Ting Hu .

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  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

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Zhou, R., Hu, T. (2024). Evolutionary Approaches to Explainable Machine Learning. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_16

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  • DOI: https://doi.org/10.1007/978-981-99-3814-8_16

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