Lime

AI Explainability: Making Models Understandable AI systems increasingly influence hiring, lending, health care, and public services. Explainability means giving people clear reasons for a model’s decisions and making how the model works understandable. Clear explanations support trust, accountability, and safer deployment, especially when money or lives are on the line. Vetted explanations help both engineers and non experts decide what to trust. Explainability comes in two broad flavors. Built-in transparency, or ante hoc, tries to make the model simpler or more interpretable by design. Post hoc explanations describe a decision after the fact, even for complex models. The best choice depends on the domain, the data, and who will read the result. ...