A Python package to assess and improve fairness of machine learning models.
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Updated
Aug 14, 2024 - Python
A Python package to assess and improve fairness of machine learning models.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切:论文、代码、框架与库
Tensorflow's Fairness Evaluation and Visualization Toolkit
Fair Resource Allocation in Federated Learning (ICLR '20)
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
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 🔎🤖🧰
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
Talks & Workshops by the CODAIT team
FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
Flexible tool for bias detection, visualization, and mitigation
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!
Julia Toolkit with fairness metrics and bias mitigation algorithms
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Official implementation of our work "Collaborative Fairness in Federated Learning."
Dataset associated with "BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation" paper
Tilted Empirical Risk Minimization (ICLR '21)
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