How does explainable artificial intelligence (XAI) improve transparency, accountability, and trust in AI systems?

Explainable artificial intelligence (XAI) enables humans to understand, interpret, and trust the decisions made by AI systems by providing explanations, insights, or visualizations of the underlying model behavior, predictions, or decision-making processes. XAI techniques include feature importance analysis, model interpretability methods, counterfactual explanations, and human-friendly representations of complex AI models, fostering transparency, accountability, and user acceptance in critical applications such as healthcare, finance, and criminal justice.