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

Estimation and inference from generalized linear models using explicit and implicit methods for bias reduction

Methods for M-estimation of statistical models

This repository contains the experiments conducted in the ICLR 2022 spotlight paper "On the Importance of Firth Bias Reduction in Few-Shot Classification".

Bias reduction in quasi likelihood estimation

Bias correction command-line tool for climatic research written in C++

Bias detection Toolkit: Chrome Extension, Python Package, SOTA research paper docs.

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".

Sampling algorithms and machine learning models to reduce bias and predict credit risk.

Critical questions to help you gain useful information, clarify the context, figure out the pain points, and overcome biases.

Location-adjusted Wald statistics

Pytorch implementation of 'Explaining text classifiers with counterfactual representations' (Lemberger & Saillenfest, 2024)

The repository contains software library for Data Augmentation Services

unbiased toxicity detection from comments

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