Reverse Dependencies of fairlearn

The following projects have a declared dependency on fairlearn:

• aequitas — The bias and fairness audit toolkit.
• aia-fairness — Various attributes inferences attacks tested against fairness enforcing mechanisms
• aif360 — IBM AI Fairness 360
• aif360-fork2 — IBM AI Fairness 360
• AutoBrewML — With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.
• azureml-responsibleai — AzureML Responsible AI package
• cortex-cli — Nearly Human Cortex CLI for interacting with model functions.
• credoai-lens — Lens: comprehensive assessment framework for AI systems
• deeploy — The official Deeploy client for Python
• equal-odds — _PACKAGE UNDER CONSTRUCTION_
• equalityml — Algorithms for evaluating fairness metrics and mitigating unfairness in supervised machine learning
• equalityml-fork2 — Algorithms for evaluating fairness metrics and mitigating unfairness in supervised machine learning
• EthicML — A toolkit for understanding and researching algorithmic bias
• genda-lens — A package for quantifying bias in Danish language models.
• lale — Library for Semi-Automated Data Science
• lares — LARES: vaLidation, evAluation and REliability Solutions
• oracle-guardian-ai — Oracle Guardian AI Open Source Project
• pre-ai-python — Microsoft AI Python Package
• pureml-evaluate — no summary
• pureml-policy — no summary
• pycaret — PyCaret - An open source, low-code machine learning library in Python.
• pycaret-nightly — Nightly version of PyCaret - An open source, low-code machine learning library in Python.
• pycaret-ts-alpha — PyCaret - An open source, low-code machine learning library in Python.
• raiwidgets — Interactive visualizations to assess fairness, explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
• skops — A set of tools to push scikit-learn based models to and pull from Hugging Face Hub
• sliceguard — A library for detecting critical data slices in structured and unstructured data based on features, metadata and model predictions.
• torchmetrics — PyTorch native Metrics
• whyshift — A package of various specified distribution shift patterns of out-of-distributoin generalization problem on tabular data, and tools for diagnosing model performance are integrated.
• XplainML — XplainML is a comprehensive Python package designed for Explainable AI (XAI) and Responsible AI practices. It provides a suite of tools and algorithms to enhance the transparency, interpretability, and fairness of machine learning models.