Alibi

github.com · checked 14 Nov 2026

#7 · AI Model Explainability Software

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How it plugs in

—Public API

NoOpen source

YesFree tier

Signal on AI Model Explainability Software5.8#7 of 29

Alibi is a Python library for machine-learning model inspection and interpretation, with explanation methods for classification and regression.

Pricing

AlibiSource-available Python library · non-production use; limited production use for non-profit educational institutions subject to conditionsFree

As github.com lists them, checked 14 Nov 2026.

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Facts

Website: github.com
Price: Free tier
Free trial: No
Confidence scores: Trust Scores and Linearity Measure provide instance-specific measures of model confidence.
Global explainers: Global methods include ALE, partial dependence, partial dependence variance, and permutation importance.
Install options: The project documents installation through pip from PyPI or GitHub, and through conda/mamba.
Integration: The usage API is modeled on scikit-learn's initialize, fit, and explain steps.
License: The repository license is Business Source License 1.1, with the stated change license of Apache License 2.0 four years after a version's release.
Local explainers: Local methods include Anchors, counterfactuals, and Integrated Gradients.
Methods: It includes black-box and white-box, local and global explanation methods.
Model compatibility: The method table distinguishes black-box explainers needing a prediction function, differentiable black-box methods, and white-box methods requiring model access.
Optional dependencies: Ray can be installed for distributed explanation computation and SHAP support is available as an install option.
Prototype method: ProtoSelect provides a distilled dataset view to help construct a 1-nearest-neighbor interpretable classifier.
Supported inputs: Its listed explanation methods cover tabular, text, and image data.

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