Techniques for making 'black box' models more interpretable and transparent.
Explainable AI (XAI) is an emerging field in machine learning that aims to make complex AI models, often referred to as 'black boxes', more understandable to humans. While models like deep neural networks can achieve incredible accuracy, their internal decision-making processes are often opaque. We know they work, but we don't always know how or why. This lack of transparency is problematic in high-stakes applications like medical diagnosis, loan approvals, or autonomous driving, where understanding the reasoning behind a decision is crucial for trust, accountability, and debugging. XAI encompasses a range of techniques to shed light on these models. Some methods are local, meaning they explain a single prediction. For example, for an image classifier that identifies a picture as a 'cat', a local explanation method like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) might highlight the specific pixels (like whiskers and pointy ears) that most contributed to that decision. Other methods aim for global interpretability, trying to explain the overall behavior of the model. This might involve summarizing the model with a simpler, more interpretable model like a decision tree. The goal of XAI is not just to satisfy curiosity but to enable developers to debug and improve models, help users trust and manage AI systems effectively, and provide a basis for auditing and regulation to ensure models are fair and robust.