Explainable Artificial Intelligence(XAI): Understanding,Implementing and unveiling the Challenges

Abstract- Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making.Explainable AI (XAI) refers to a set of techniques and methods designed to make the outputs and decision-making processes of artificial intelligence (AI) models understandable to human users. As AI systems become more prevalent and influential, particularly in high-stakes domains like security,healthcare and military, the need for transparency and interpretability becomes crucial. Traditional AI models, such as deep neural networks, are often criticized for their "black box" nature, where the reasoning behind decisions is opaque and inaccessible.A black box model has high accuracy but low interpretablity and explainability.The paper deals with the concept of XAI and its implementation on different sectors and as well as the implementation challenges.