What is the role of explainability in ML, and how can it be advanced to build user trust in complex systems?

ANSWER: Explainability in ML provides insight into how models make decisions, promoting transparency and increasing user trust. Advancing it involves creating interpretable models and applying techniques like feature importance, SHAP values, and LIME. Clear communication of ML processes, including potential biases and limitations, also enhances trustworthiness, ensuring users understand and confidently rely on complex systems for decision-making.