Alibi is an open-source Python library that supports various interpretability techniques and a broad array of explanation types. The README already provides an overview of the supported methods and when they are applicable. The following table with supported methods is copied from the README (slightly abbreviated):

Supported methods

Method Models Explanations Classification Regression Tabular Text Images Categorical features
ALE BB global
Anchors BB local
CEM BB* TF/Keras local
Counterfactuals BB* TF/Keras local
Prototype Counterfactuals BB* TF/Keras local
Integrated Gradients TF/Keras local
Kernel SHAP BB local global
Tree SHAP WB local global

The README also explains the keys:

  • BB - black-box (only require a prediction function)
  • BB* - black-box but assume model is differentiable
  • WB - requires white-box model access. There may be limitations on models supported
  • TF/Keras - TensorFlow models via the Keras API
  • Local - instance specific explanation, why was this prediction made?
  • Global - explains the model with respect to a set of instances

For more detailed information on the supported methods, see the algorithm overview.