Machine Learning Explainability (XAI), Simply Explained

As machine learning models become increasingly complex, their decisions often become harder to understand and interpret.

Models like deep neural networks, ensemble methods, and large-scale transformers can achieve remarkable predictive performance, but they mostly operate as black boxes.

The models themselves do not offer much insight into exactly how the inputs are transformed into outputs.

Machine Learning Explainability, commonly referred to as XAI (Explainable Artificial Intelligence), addresses this challenge by providing methods and tools to interpret, understand, and trust the decisions made by machine learning models.