Explainable AI (XAI): Building Interpretable Models

XAI is a framework that enables businesses to understand the output of AI or ML algorithms, providing transparency and feedback to improve model performance. Its importance lies in reducing errors, curbing model bias, promoting confidence and compliance, improving model performance, facilitating informed decision-making, and increasing brand value. Practical techniques to implement XAI include LIME, fairness and bias testing, and SHAP, which provide local approximations, assess feature importance, and explain predictions. Despite its benefits, XAI faces challenges such as limited model availability, accuracy vs. interpretability trade-offs, and the need for further adaptation across industries. As XAI continues to evolve, it has the potential to solve impactful problems for the greater good.