aequitas
An open-source toolkit for auditing bias and experimenting with fairness methods in machine learning models.
What is aequitas?
Aequitas is an open-source bias auditing and Fair ML toolkit that helps data scientists and researchers detect and mitigate bias in machine learning models. It provides tools to audit model predictions across sensitive attributes and experiment with fairness-enhancing methods throughout the ML pipeline. The toolkit supports binary classification tasks and integrates various pre-, in-, and post-processing techniques to promote equitable outcomes.
Data scientists, machine learning researchers, and policymakers working on binary classification models who need to assess and improve fairness. It's also suitable for teams implementing responsible AI practices in production systems.
Developers choose Aequitas for its comprehensive, all-in-one approach to fairness—combining bias auditing with mitigation experimentation in a single, extensible toolkit. Its integration of multiple Fair ML methods and visualization tools simplifies the process of evaluating and enhancing model equity.
Overview
Bias Auditing & Fair ML Toolkit
Use Cases
Best For
- Auditing binary classification models for bias across sensitive attributes like race or gender
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