An Overview of Explainable Artificial Intelligence (XAI) and Its Application
TL;DR
XAI provides a powerful framework for responsible AI development, challenges such as the performance-interpretability trade-off, lack of standardized evaluation metrics, and potential for human misinterpretation remain areas of active research.
Abstract
This assessment paper emphasise about newb technology of Explainable Artificial Intelligence (XAI) is an emerging and vital field of research that addresses the "black box" problem prevalent in modern machine learning. As AI systems become more complex and integrated into high-stakes domains such as healthcare, finance, and criminal justice, their inherent opacity raises critical concerns regarding transparency, trust, and accountability. The primary goal of XAI is to provide methods and techniques that enable human users to understand, interpret, and appropriately trust the decisions and predictions made by AI algorithms. While XAI provides a powerful framework for responsible AI development, challenges such as the performance-interpretability trade-off, lack of standardized evaluation metrics, and potential for human misinterpretation remain areas of active research. Ultimately, XAI is a critical step toward creating a symbiotic relationship between humans and AI, where intelligent systems operate not just with high performance but with ethical and transparent reasoning.