Unveiling the Veil: Exploring Challenges, Applications, and Cutting-Edge Techniques in Explainable Artificial Intelligence

Abstract

In an era dominated by intricate artificial intelligence (AI) models, the demand for transparency and interpretability has never been more pressing. The article explores Explainable Artificial Intelligence (XAI), focusing on its techniques, application areas, and challenges. It investigates a number of XAI techniques aimed at shedding light on complex model decision-making processes, including Shapley Additive Explanations, Local Interpretable Model-agnostic Explanations, Saliency Maps, Class Activation Maps, and others. The discussion moves on to real-world applications where XAI is transforming critical sectors such as healthcare, finance, legal systems, and human resources. Simultaneously, the challenges associated with these techniques are discussed, emphasizing considerations for practitioners and researchers. The article adds to the continuing discussion about the significance of transparency and interpretability in artificial intelligence systems._x000D_ Keywords: Interpretability, Explainable AI, XAI, Complex neural network.