Explore how Explainable AI (XAI) works and its importance in making AI decisions transparent and understandable.
XAI refers to AI systems designed to provide clear, understandable insights into how they make decisions.
XAI enhances trust in AI by making decision-making processes transparent, which is crucial for applications in healthcare, finance, and more.
XAI uses techniques like feature importance, model interpretability, and decision trees to explain AI decisions.
Common XAI techniques include LIME, SHAP, and attention mechanisms, which help break down complex models.
XAI provides accountability, improves AI model accuracy, and facilitates compliance with regulations.
XAI is used in healthcare to explain diagnoses, in finance for credit scoring, and in autonomous vehicles for decision transparency.
XAI is used in healthcare to explain diagnoses, in finance for credit scoring, and in autonomous vehicles for decision transparency.