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.

WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰

NeurIPS 2019 Paper: RUBi : Reducing Unimodal Biases for Visual Question Answering

[ICML 2022] Channel Importance Matters in Few-shot Image Classification

Tensorflow implementation of Learning Not to Learn (CVPR 2019)

This repository contains the firth bias reduction experiments on the few-shot distribution calibration method conducted in the ICLR 2022 spotlight paper "On the Importance of Firth Bias Reduction in Few-Shot Classification".

Pytorch implementation of 'Explaining text classifiers with counterfactual representations' (Lemberger & Saillenfest, 2024), ECAI 2024 - 27th European conference on AI

The repository contains software library for Data Augmentation Services

A method to preprocess the training data, producing an adjusted dataset that is independent of the group variable with minimum information loss.