SHAP (SHapley Additive exPlanations) is a popular library for model agnostic interpretability. It leverages Shapley values from cooperative game theory to assign contributions to each feature in a prediction, providing a unified measure of feature importance.

LIME (Local Interpretable Model-agnostic Explanations) is designed to explain the predictions of machine learning models on a local level. It generates locally faithful and interpretable surrogate models to approximate the behavior of the black-box model in the vicinity of a specific prediction.

ELI5 (Explain Like I'm 5) is a library that provides simple and human-readable explanations for machine learning models. It supports various models and offers insights into feature importance and model predictions.

InterpretML is an open-source library that simplifies the interpretation of machine learning models. It provides a suite of explainability techniques, including feature importance, SHAP values, and partial dependence plots.

Alibi is a library dedicated to providing transparency and interpretability for machine learning models. It offers a range of techniques for model explanation, feature attribution, and outlier detection. Alibi is designed to be model-agnostic, meaning it can be applied to various machine learning models, including deep learning architectures. It includes functionalities for counterfactual explanations, allowing users to understand how changes in input features impact model predictions. With Alibi, users can gain insights into the decision boundaries of complex models, fostering trust and understanding in AI systems.

Skater is an explainability library designed to enhance model interpretability by generating human-understandable explanations for machine learning models. It offers a variety of techniques, including rule-based explanations and feature importance analysis. Skater supports both tabular and image data, making it versatile for different types of models and applications. With Skater, users can gain insights into the decision-making process of machine learning models, fostering trust and understanding in AI systems.