Explainable AI: Methods, Challenges, and Future Directions

Abstract

As artificial intelligence (AI)[1] systems become increasingly complex and pervasive, the need for transparency and interpretability has become a critical concern. Explainable AI (XAI)[2, 3] seeks to bridge the gap between opaque machine learning models and human users by providing insights into the decision-making processes of AI systems. This editorial explores the various methods employed in XAI, the challenges faced in achieving interpretability, and potential future directions for the field.

The rapid adoption of AI in critical domains such as healthcare, finance, and criminal justice has raised concerns about the "black-box" nature of many AI models[4]. While these models often achieve high accuracy, their decision-making processes remain obscure, making it difficult to diagnose errors, ensure fairness, and build user trust. Explainable AI aims to address these concerns by developing techniques that offer transparency and interpretability without compromising performance.