SeldonIO/alibi

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2644 stars · 266 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Alibi is a Python library providing algorithms for explaining and interpreting machine learning models, including black-box, white-box, local, and global explanation methods for classification and regression. It supports techniques like anchor explanations, integrated gradients, and counterfactuals across tabular, image, and text data.

Use cases

• explain predictions of a black-box ML model
• generate counterfactual explanations for classifier decisions
• compute feature attributions like integrated gradients for text models
• interpret image classification model predictions
• audit model decisions for regulatory compliance
• understand which features drive a regression model's output

When to choose

• you need production-quality implementations of explanation algorithms in Python
• you want to explain models across tabular, image, and text modalities
• you need both local and global interpretability methods in one library

When to avoid

• you need outlier or drift detection - use the sister project alibi-detect instead
• you need a no-code or GUI-based explainability tool
• your license requirements are incompatible with the Business Source License 1.1

Facets

library · maturity active

machine-learning nlp computer-vision machine-learning data-science artificial-intelligence python cross-platform explainability xai interpretability counterfactual-explanations model-inspection shap anchor-explanations

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