A Python package to assess and improve fairness of machine learning models.
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Updated
Jun 13, 2024 - Python
A Python package to assess and improve fairness of machine learning models.
Fair Resource Allocation in Federated Learning (ICLR '20)
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.
Code accompanying our papers on the "Generative Distributional Control" framework
Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
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!
Official implementation of our work "Collaborative Fairness in Federated Learning."
Tilted Empirical Risk Minimization (ICLR '21)
A tool for gender bias identification in text. Part of Microsoft's Responsible AI toolbox.
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 Python toolkit for analyzing machine learning models and datasets.
FairBatch: Batch Selection for Model Fairness (ICLR 2021)
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning [AACL 2022]
Source code for KDD 2020 paper "Algorithmic Decision Making with Conditional Fairness".
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]
Variational Fair clustering
Code to accompany NeurIPS paper https://arxiv.org/abs/2006.08564
Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
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