Explainable AI: Assessing Methods to Make AI Systems More Transparent and Interpretable

Abstract

As artificial intelligence (AI) systems continue to evolve and play an increasingly prominent role in various facets of society, the need for transparency and interpretability becomes paramount. The lack of understanding surrounding complex AI models poses significant challenges, especially in critical domains such as healthcare, finance, and autonomous systems. This paper aims to explore and assess various methods employed to enhance the transparency and interpretability of AI systems, collectively known as Explainable AI (XAI). The first part of the paper provides an overview of the current landscape of AI technologies and highlights the growing demand for explainability. It discusses the ethical, legal, and societal implications of opaque AI systems, emphasizing the importance of building trust among users and stakeholders.