As AI models become increasingly complex, understanding how they make decisions is crucial. This is especially true in fields like healthcare, finance, and law, where transparency and accountability are paramount. Explainable AI (XAI) helps by making AI models more interpretable. This blog will introduce seven tools and techniques for model interpretability, providing software developers with practical ways to demystify AI.
1. LIME (Local Interpretable Model-Agnostic Explanations)
What Is LIME?
LIME is a popular tool for interpreting complex models. It works by approximating the model locally with an interpretable model, such as a linear regression or decision tree.