Fairness Python Packages

fairlearn

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

codecarbon

Track emissions from Compute and recommend ways to reduce their impact on the environment.

raiutils

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.

aif360

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.

dalex

moDel Agnostic Language for Exploration and eXplanation

erroranalysis

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.

responsibleai

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.

raiwidgets

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.

bias-detection-mcp

AI bias detection + fairness assessment MCP. Required by EU AI Act Article 10 + NYC Local Law 144. MIT

trustmodel

Score any AI for trust — Eval, Monitor, Govern. 10 trust dimensions, one free API key (5 credits / $500).

jurity

[ACM 2024] Jurity: Fairness & Evaluation Library

howfairis

Command line tool to analyze a GitHub or GitLab repository's compliance with the fair-software.eu recommendations

ethicml

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

infairness

PyTorch package to train and audit ML models for Individual Fairness

fairgbm

Train Gradient Boosting models that are both high-performance *and* Fair!

rai-governance-platform

Open-source bias testing framework for LLMs — detect gender, racial & cultural bias in any language model via TF-IDF divergence scoring

langfair

LangFair is a Python library for conducting use-case level LLM bias and fairness assessments

folktexts

Evaluate uncertainty, calibration, accuracy, and fairness of LLMs on real-world survey data!

flgo

An experimental platform for federated learning.

rai-test-utils

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.

nlp-feature-extractors

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.

oracle-automlx

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.

nlpguard

NLPGuard: A Framework for Mitigating the use of Protected Attributes in NLP

responsibleai-vision

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.