How do explainable AI (XAI) techniques enhance the transparency and interpretability of machine learning models?

Explainable AI (XAI) techniques provide insights into how machine learning models make predictions by generating human-interpretable explanations. These techniques, such as feature importance analysis, model visualization, and rule extraction, help users understand the underlying factors driving model predictions and identify potential sources of bias or error.