Explainable Artificial Intelligence (xAI) focuses on developing AI systems that provide transparent and understandable explanations for their decisions.

xAI aims to address the "black box" problem in AI, increasing trust and accountability.

It enables users to understand how AI algorithms arrive at specific outcomes, promoting transparency.

xAI techniques include rule-based systems, decision trees, and model-agnostic methods like LIME and SHAP.

xAI techniques include rule-based systems, decision trees, and model-agnostic methods like LIME and SHAP.

By providing explanations, xAI helps identify bias, errors, or unethical behavior in AI systems.

xAI is crucial for regulatory compliance, ethical considerations, and fostering user acceptance of AI technologies.