EqualityAI/EqualityML
Evidence-based tools and community collaboration to end algorithmic bias, one data scientist at a time.
This project provides practical tools and guidance for data scientists to detect and reduce unfairness in their machine learning models. It helps ensure that model outputs, such as predictions or classifications, are equitable across different groups of people. Data scientists can input their datasets and models, then use the provided metrics and methods to identify and mitigate bias, leading to more responsible and fair AI systems.
No commits in the last 6 months. Available on PyPI.
Use this if you are a data scientist building or deploying machine learning models and want to ensure they treat all user groups fairly and do not perpetuate or amplify societal biases.
Not ideal if you are looking for a general-purpose machine learning library without a specific focus on fairness, or if you are not working with tabular or structured data that influences decisions about people.
Stars
35
Forks
2
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Oct 29, 2023
Commits (30d)
0
Dependencies
10
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/EqualityAI/EqualityML"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations...
holistic-ai/holisticai
This is an open-source tool to assess and improve the trustworthiness of AI systems.
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment...
datamllab/awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources