Explainable AI: Bridging the Gap between Performance and Transparency

Abstract

In recent years, Artificial Intelligence (AI) has made groundbreaking strides, delivering unprecedented performance across various domains, including healthcare, finance, autonomous systems, and natural language processing. However, the increasing reliance on complex, black-box AI models has led to growing concerns regarding their interpretability, trustworthiness, and ethical implications. Explainable AI (XAI) emerges as a response to these concerns, aiming to make AI systems more transparent, understandable, and accountable to humans. This paper explores the core principles, methodologies, and challenges of Explainable AI, analyzing its role in bridging the gap between model performance and user trust. By examining the evolution of XAI, its technical frameworks, evaluation strategies, and application scenarios, we shed light on how explainability can coexist with high-performing AI, facilitating human-centric AI development and ethical deployment. We conclude with insights into future directions for research and implementation, emphasizing the necessity of interdisciplinary collaboration to ensure responsible and explainable AI.