fairlearn/fairlearn

Health v2 · maintenance only

Adoption not part of the score

2277 stars · 513 forks observed · 2026-08-28

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

Fairlearn is a Python package for assessing and mitigating fairness issues in machine learning models. It provides fairness metrics for evaluating model behavior across groups and algorithms for reducing unfairness under group fairness definitions.

Use cases

• measure fairness metrics across demographic groups in a classifier
• mitigate bias in a loan default prediction model
• compare models by accuracy and fairness trade-offs
• detect quality-of-service disparities in an ML system
• apply group fairness constraints during model training
• audit hiring or admissions models for allocation harms

When to choose

• you need quantitative fairness metrics and mitigation algorithms in a Python/scikit-learn workflow
• you want to assess group-level harms in classification or regression models
• you need an actively maintained, MIT-licensed fairness toolkit

When to avoid

• you need causal fairness analysis or individual fairness definitions
• you expect fairness to be solved purely by running code without sociotechnical context
• you need fairness tooling outside the Python ecosystem

Facets

library · maturity active

machine-learning data-science monitoring machine-learning artificial-intelligence data-science python cross-platform fairness responsible-ai group-fairness bias-mitigation fairness-metrics scikit-learn algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="fairlearn/fairlearn")