copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
Fairlearn Fairlearn is an open-source, community-driven project to help data scientists improve fairness of AI systems Learn about AI fairness from our guides and use cases
FairLearn - GitHub Pages Fairlearn provides developers and data scientists with capabilities to assess the fairness of their machine learning models and mitigate unfairness Assess existing models and train new models with fairness in mind Compare models and make trade-offs between fairness and model performance
fairlearn · PyPI Fairlearn contains mitigation algorithms as well as metrics for model assessment Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage
Fairlearn: A toolkit for assessing and improving fairness in AI We introduce Fairlearn, an open source toolkit that empowers data scientists and developers to assess and improve the fairness of their AI systems Fairlearn has two components: an interactive visualization dashboard and unfairness mitigation algorithms
Fairlearn - GitHub Governance documents for the Fairlearn Organization Fairlearn has 6 repositories available Follow their code on GitHub
fairlearn at main - GitHub Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues Fairlearn contains mitigation algorithms as well as metrics for model assessment
Fairlearn - edwinwenink. github. io The documentation of fairlearn is excellent and provides a good introduction to the topic of fairness in AI It is emphasized that fairness algorithms are no plug-and-play technical solutions, but require serious thought about the context of the data and the problem at hand