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Machine learning has the remarkable capability to uncover intricate patterns and relationships within
data. However, the consequential decisions made based on these model predictions can profoundly
affect human lives. As machine learning models find their way into high鈥恠takes domains such as
medicine, job hiring, and criminal justice, concerns about fairness, transparency, and accountability
have emerged. In response, there is a growing need not only to create highly accurate prediction
models but also to comprehend and elucidate the inner workings of these predictive systems.

Evolutionary computing, a versatile meta鈥恖earning approach, can generate innovative, multi鈥恛bjective,
and diverse solutions to optimization and learning challenges. It holds exceptional promise in the realm
of crafting solutions for Explainable AI (XAI). In this talk, we discuss the landscape of explainability and
its associated concepts. We highlight the potential of evolutionary computing as a means of crafting
comprehensive explanations for machine learning.