Explainable Artificial Intelligence (XAI): Bridging the Gap Between Accuracy and Interpretability

Abstract

The increasing demand for trust, accountability, and transparency in sophisticated machine learning systems has led to the rise of Explainable AI (XAI) as a significant field of study. Their "black-box" aspect frequently limits interpretability and hinders their adoption in high-stakes decision-making situations, even if advanced models, especially deep learning architectures, have accomplished outstanding accuracy across domains including healthcare, autonomous systems, and finance. It is a major issue for researchers and practitioners alike to strike a balance between model transparency and prediction performance. To close this gap, XAI is working on ways to explain model behavior to people in a way that does not sacrifice accuracy. To shed light on the prediction generation process, researchers have turned to techniques like feature importance analysis, attention processes, model-agnostic explanation methods (like LIME and SHAP), and intrinsically interpretable models. Model judgments can be validated, biases can be detected, and regulatory and ethical compliance can be assured with the use of these methodologies.