A Python package to assess and improve fairness of machine learning models.
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
Fair Resource Allocation in Federated Learning (ICLR '20)
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!
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments
Code accompanying our papers on the "Generative Distributional Control" framework
Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
A Python toolkit for analyzing machine learning models and datasets.
Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.
Tilted Empirical Risk Minimization (ICLR '21)
Official implementation of our work "Collaborative Fairness in Federated Learning."
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.
A tool for gender bias identification in text. Part of Microsoft's Responsible AI toolbox.
[KDD2021] Federated Adversarial Debiasing for Fair and Transferable Representations: Optimize an adversarial domain-adaptation objective without adversarial or source data.
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning [AACL 2022]
A fairness library in PyTorch.
PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
FairBatch: Batch Selection for Model Fairness (ICLR 2021)
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