Explainability

Explainability in AI refers to techniques that help users comprehend the rationale behind a model's outputs. It is essential for building trust in AI systems, especially in sensitive applications like healthcare or finance, where understanding the model's decision-making process can be critical. Techniques for enhancing explainability include feature importance analysis, visualizations, and rule-based explanations.