Alibi

• Values: { explainability }
  • Explanation type: { global surrogate local surrogate example-based Shapley value anchor contrastive counterfactual ALE gradient-based }
• Categories: { model-specific model-agnostic }
• Stage: { post-processing }
• Repository: https://github.com/SeldonIO/alibi
• Tasks: { classification regression }
• Input data: { image text tabular }
• Licence: Apache License 2.0
• Languages: { Python }
• Frameworks: { Keras }

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.