The Role of Explainable AI in Enhancing Trust and Transparency in Machine Learning Models

Abstract

Explainable AI (XAI) plays a crucial role in enhancing trust and transparency in machine learning models by making the decision-making processes of these models more understandable to humans. As AI systems are increasingly used in critical areas such as healthcare, finance, and law enforcement, the need for transparency becomes paramount. XAI provides insights into how models arrive at specific decisions, allowing users to understand and trust the outputs. This transparency helps to identify and mitigate biases, ensure fairness, and improve accountability, which are essential for the ethical deployment of AI technologies. By demystifying the "black box" nature of many machine learning models, XAI fosters greater user confidence and facilitates broader adoption of AI systems in sensitive and regulated industries.