fairlearn and AIF360
These are complementary tools that can be used together, as fairlearn focuses on fairness assessment and mitigation through constraints-based optimization, while AIF360 provides a broader toolkit of bias metrics, explainability for those metrics, and diverse mitigation algorithms that address different fairness definitions and use cases.
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 AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
This project helps data professionals evaluate and improve the fairness of their machine learning models. You input your datasets and models, and it provides metrics to detect potential biases and offers algorithms to reduce them. This is for data scientists, machine learning engineers, and risk managers who need to ensure their AI systems are equitable.
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