Advancements in Explainable AI: Bridging the Gap Between Interpretability and Performance in Machine Learning Models

The growing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in critical decision-making areas such as healthcare, finance, and autonomous systems has raised concerns regarding the interpretability of these models. While deep learning and other advanced ML models deliver high accuracy, their "black box" nature makes it difficult to explain their decision-making processes. Explainable AI (XAI) aims to bridge this gap by introducing methods that enhance transparency without significantly compromising performance. This paper explores key advancements in XAI, including model-agnostic and model-specific interpretability techniques, and evaluates their effectiveness in balancing model explainability and performance. We conduct an empirical analysis on commonly used XAI techniques, present a case study on AI-assisted healthcare diagnostics, and analyze stakeholder perspectives through a structured questionnaire. Our findings suggest that while XAI methods improve interpretability and stakeholder trust, they often come with computational and accuracy trade-offs. The study also highlights the challenges and opportunities in integrating XAI into real-world applications and provides recommendations for future research.