Reference no: EM133736202

Question: Define Explainable AI Based on the Manuscript Provided (200 words)

Explainable Artificial Intelligence (XAI) refers to AI systems that can provide clear, understandable explanations for their decisions and predictions to human users. The goal of XAI is to enhance the transparency, trustworthiness, and accountability of AI systems, especially when used in high-stakes applications such as healthcare, finance, and security. Unlike traditional "black box" AI models that operate opaquely, XAI models offer insights into their inner workings, making it possible for users to understand how specific outcomes are reached.

The concept of XAI emerged to address the growing need for interpretable AI systems. The Defense Advanced Research Projects Agency (DARPA) coined the term "Explainable AI," emphasizing the importance of making AI decision-making processes comprehensible to non-expert users. XAI employs various techniques to elucidate the mechanisms behind AI models, ranging from simple linear regression models that are inherently interpretable to complex post-hoc approaches like Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). These techniques can be applied to both global models, which provide a broad understanding of AI behavior, and local models, which explain individual predictions.