Neuro-Evolutionary Approaches for Explainable AI (XAI)

Abstract

Explainable Artificial Intelligence (XAI) is paramount for building trust and understanding in machine learning models, particularly in complex domains. Traditional XAI methods face challenges when applied to neural networks evolved through Neuro-Evolutionary Algorithms (NEAs), limiting their effectiveness in providing transparent insights into model decision-making processes. This research introduces a novel framework that integrates NEAs with XAI techniques, aiming to enhance the explainability of evolved neural network architectures. By combining the adaptability of neuro-evolution with interpretability-focused methodologies, the proposed approach addresses the inherent opacity of evolved models. This article presents an in-depth exploration of the framework's principles, detailing its application to neural network evolution and the incorporation of state-of-the-art XAI techniques. Experimental results showcase the effectiveness of the neuro-evolutionary XAI framework in producing models that not only exhibit high performance but also offer interpretable and transparent decision-making processes. The findings highlight the potential of neuro-evolutionary approaches in advancing the field of XAI, paving the way for more trustworthy and understandable AI systems in complex applications. As artificial intelligence (AI) and machine learning (ML) increasingly infiltrate critical domains, the lack of explainability in complex models creates concerns about accountability, trust, and fairness. Explainable AI (XAI) seeks to address this issue by understanding how models make decisions and providing insights into their reasoning. This research delves into a promising avenue within XAI: neuro-evolutionary approaches. Inspired by the brain's learning mechanisms, neuro-evolutionary algorithms offer unique capabilities for tackling explainability challenges.