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.

A Python package to assess and improve fairness of machine learning models.

A collection of Codex skills for IEEE-style academic writing, review, experiments, figures, LaTeX, citations, and paper reading.

A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency