THE ROLE OF EXPLAINABLE AI IN MACHINE LEARNING MODEL INTERPRETABILITY

Abstract

This work investigates the contribution of Explainable AI to the interpretability of ML models by analyzing several methods of enabling model interpretability and how this benefit stakeholders through increased trust and usability. Explainable Artificial Intelligence has become an important part of the research area in machine learning to cope with the diamond's black box of verbose models. ML applications increasingly target sensitive sectors like healthcare, finance and law enforcement. It is making transparency and interpretability critical for building trust, enhancing decision-making and meeting regulatory requirements. This study is carried out using the mixed method, adopting the systematic literature review method alongside an empirical analysis of explainability techniques. A case study is performed in a real-world application, involving user perceptions and model performance trade-offs when using XAI methods. The discoveries clarify that while explainable artificial intelligence has procedures expanded the interpretability of a model. There was commonly a compromise between precision and explicability. This work highlights that the choice of explainable artificial Intelligence has method driven by the needs of the use case and goals of stakeholders. The task-specific efforts in developing scalable, such as, consistent, explanatory and real-time applicable. Explainable Artificial Intelligence has techniques are  essential to promoting even wider integration of XAI methodology in ML-driven decision-making systems.

Keywords

 Explainable AI , Machine Learning Interpretability, Model Transparency, Feature Importance, Trust in AI, Black-Box Models, Rule-Based Explanations, Decision-Making