nick-jhlee/fair-manifold-pca
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold (AAAI 2022)
This project helps data scientists and machine learning engineers transform high-dimensional data into a smaller, more manageable form while actively reducing bias related to sensitive attributes like gender or race. It takes in your original dataset, along with sensitive group labels and optionally a downstream classification label, to produce a lower-dimensional representation that is fairer according to your chosen criteria. This tool is for practitioners building predictive models who need to ensure their data's dimensionality reduction steps do not inadvertently amplify or introduce unfairness.
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Use this if you are performing dimensionality reduction (like PCA) on a dataset and need to explicitly mitigate bias against specific protected groups present in your data, ensuring fairness in downstream analyses or models.
Not ideal if your primary goal is simply general-purpose dimensionality reduction without a specific concern for fairness metrics or if you need a solution fully implemented in Python (this tool is predominantly in MATLAB).
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Language
C++
License
MIT
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Last pushed
Sep 27, 2022
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