A Review on Explainable Ai (Xai)

Abstract

Explainable AI (XAI) plays a pivotal role in addressing the opacity of complex AI models by enhancing transparency and interpretability, consequently fostering trust and acceptance in their decision-making processes. This paper explores diverse methods and techniques within XAI, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), elucidating their applications across various domains. By shedding light on the interpretability mechanisms of AI, this research underscores their significance in healthcare, finance, autonomous systems, and predictive analytics. Moreover, it delves into the challenges, including the trade-offs between interpretability and model performance, ethical considerations, and the regulatory landscape. Ultimately, this paper advocates for the integration of XAI techniques to advance transparency and comprehension in intricate AI models, paving the way for responsible and accountable AI deployment.Certainly! Let's expand each section with more detailed content