fairness-ai

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.

A Python package to assess and improve fairness of machine learning models.

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.

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 🔎🤖🧰

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Talks & Workshops by the CODAIT team

Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.

PyTorch package to train and audit ML models for Individual Fairness

[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models

[ACL 2020] Towards Debiasing Sentence Representations

Counterfactual Local Explanations of AI systems

R package for computing and visualizing fair ML metrics

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

Multi-Calibration & Multi-Accuracy Boosting for R

Examples of unfairness detection for a classification-based credit model

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.

[ICMLA 2021] Jurity: Fairness & Evaluation Library