Fairness Ml Python Packages

fairlearn

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

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.

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.

ethicml

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

fairgbm

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

langfair

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

fairness-indicators

Tensorflow's Fairness Evaluation and Visualization Toolkit

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.

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.

thetiscore

Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects.

trustlens

Audit ML models beyond accuracy — calibration, fairness, latent health, and deployment verdicts.

responsibleai-text

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.

credoai-lens

Lens: comprehensive assessment framework for AI systems

fairlearn-fhe

Drop-in encrypted Fairlearn metrics over CKKS. Same API surface; ciphertext arithmetic via TenSEAL or OpenFHE.

tensorboard-plugin-fairness-indicators

Tensorflow's Fairness Evaluation and Visualization Toolkit

dnikit

A Python toolkit for analyzing machine learning models and datasets.

wefe

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!

research

A collection of machine learning and reinforcement learning algorithms for my PhD research and/or personal education.

genbit

No description available

equalityml

Evidence-based tools and community collaboration to end algorithmic bias, one data scientist at a time.

fairdo

A Python package for mitigating bias in tabular data.