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.

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

Official implementation of our work "Collaborative Fairness in Federated Learning."

Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021

Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency

Paper lists about 'Constitutional AI System' or 'AI under Ethical Guidelines'

Fairness accountable regression in Matlab

Official implementation of "FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records" (MLHC 2024)

implementation of fair dummies

Tensorflow constrained optimization with different Deep Learning Models

Package implementing methods developed in "Preventing Fairness Gerrymandering" [ICML '18], "Rich Subgroup Fairness for Machine Learning" [ FAT* '19]. active development fork @algowatchupenn

Mitigate machine learning bias to ensure data ethics in U.S. national home mortgage dataset.

Human in the Loop

Fairness-aware Data Mining