Explainability

Explainable Machine Learning refers to methods that make AI decisions understandable to humans.

Integrated Gradients is a method for attributing model predictions to input features in neural networks.

Interpretability research focuses on making AI models understandable to humans.

Local Interpretable Model-Agnostic Explanations (LIME) provide insights into machine learning model predictions by approximating them locally.

Model Explainability refers to the degree to which an AI model's decisions can be understood by humans.