Explainable AI: Bridging the Gap Between Black Box Models and Interpretability

Abstract

Explainable Artificial Intelligence (XAI) represents a critical step forward in the development and deployment of machine learning models. As AI systems become increasingly integral to decision-making processes across diverse industries, their lack of transparency and interpretability poses significant challenges. This paper explores the essence of XAI, its necessity, current advancements, and future directions. By examining both technical and ethical dimensions, we highlight how XAI can enable stakeholders to better understand, trust, and effectively use AI systems. The ultimate aim is to bridge the gap between black-box models and interpretability, fostering responsible AI adoption.