EthicalML/xai

Health v2 · maintenance only

Adoption not part of the score

1259 stars · 186 forks observed · 2026-08-28

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

XAI is a Python library from the Institute for Ethical AI & ML that provides tools for analysing and evaluating machine learning datasets and models with explainability in mind. It supports bias evaluation, class imbalance handling (upsampling/downsampling), and feature importance analysis across the three steps of explainable ML: identifying, mitigating, and evaluating.

Use cases

• evaluate bias in machine learning datasets
• measure feature importance of trained models
• handle class imbalance with upsampling or downsampling
• explain model predictions for fairness audits
• analyse datasets for responsible ML practices
• identify discrepancies causing sub-optimal model performance

When to choose

• you need a lightweight Python toolbox for bias and imbalance analysis
• you want explainability tooling aligned with responsible ML principles
• you are doing exploratory fairness evaluation in Jupyter notebooks

When to avoid

• you need production-grade, actively maintained explainability tooling
• you need model-agnostic explainers like SHAP or LIME with broad model support
• you require support for modern Python versions beyond 3.7

Facets

library · maturity experimental

machine-learning data-science data-visualization benchmarking machine-learning artificial-intelligence data-science python explainable-ai xai bias-evaluation interpretability feature-importance imbalance fairness algorithms

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