Explainable AI Toolkit

A comprehensive toolkit for implementing explainability in machine learning models, providing both training-time interpretability and post-hoc analysis capabilities.

AI models, especially in healthcare and finance, need to be interpretable and trustworthy. Practitioners need tools to understand model decisions, debug performance issues, and meet regulatory requirements for explainability.

Created a modular toolkit that integrates with popular ML frameworks, providing feature importance analysis, attention visualization, counterfactual explanations, and model-agnostic interpretation methods. The toolkit supports both global model understanding and local prediction explanations.