fairlearn
EthicML
| fairlearn | EthicML | |
|---|---|---|
| 6 | 1 | |
| 1,795 | 24 | |
| 1.1% | - | |
| 8.0 | 9.3 | |
| 27 days ago | 2 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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fairlearn
- Fairlearn
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Open source projects to work on AI bias
I'm involved in the Fairlearn project, and we always love getting new contributors. We have a bunch of open issues, ranging from new functionality to writing documentation, so feel free to take a look and see if there is something you would like to work on.
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In your experience, are AI Ethics teams valuable/effective? [D]
I'm involved with the Fairlearn project, so once I figure out what's necessary from a company policy-side, my plan is to incorporate these methods into Fairlearn one day.
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Building a Responsible AI Solution - Principles into Practice
Besides the existing monitoring solution mentioned in the section above, we were also took inspiration from continuous integration and continuous delivery (CI/CD) testing tools like Jenkins and Circle CI, on the engineering front, and existing fairness libraries like Microsoft's Fairlearn and IMB's Fairness 360, on the machine learning side of things.
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Ideas on how to use my data skills for a good cause?
Another commenter mentioned contributing to open-source tools. If you're interested in going that route, I'm involved in the Fairlearn project, and we could always benefit from a good data engineer.
EthicML
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[R] An overview of some available Fairness Frameworks & Packages
These are all great tools. I found though that there wasn't one package with the flexibility of what we needed in my research group for work in this area, so we wrote EthicML. Some of you may also find it useful too.
What are some alternatives?
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.
responsible-ai-toolbox - 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.
verifyml - Open-source toolkit to help companies implement responsible AI workflows.
DALEX - moDel Agnostic Language for Exploration and eXplanation
model-card-toolkit - A toolkit that streamlines and automates the generation of model cards
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
Jenkins - Jenkins automation server
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models