fairlearn and fairlib
Fairlearn is a mature, production-ready fairness auditing framework with broad adoption, while Fairlib appears to be an early-stage alternative implementation focusing specifically on classification tasks, making them direct competitors for the same use case of fairness assessment.
About fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
This tool helps AI system developers and data scientists evaluate and improve the fairness of their machine learning models. You provide an existing AI model and information about the groups you want to assess for fairness, and it outputs metrics quantifying potential biases and offers algorithms to mitigate unfairness. It's designed for anyone building AI systems for sensitive applications like hiring or lending.
About fairlib
Libr-AI/fairlib
A framework for assessing and improving classification fairness.
This framework helps data scientists and machine learning engineers build more equitable classification models. It takes in structured data, text, or images used for classification tasks and helps identify and reduce biases related to protected characteristics. The output is a more fair and less discriminatory classification model.
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