verifyml
AIF360
verifyml | AIF360 | |
---|---|---|
1 | 6 | |
22 | 2,311 | |
- | 1.1% | |
0.0 | 7.2 | |
about 2 years ago | 13 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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verifyml
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Building a Responsible AI Solution - Principles into Practice
Interested readers should check out the VerifyML website, docs or Github code. Feel free to create a Github issue and drop any suggestions or feedback over there! VerifyML is proudly open-sourced and created by a small tech startup. I would like to think that more companies are going to see responsible AI as a comparative advantage or requirement and having an ecosystem of solutions that are not controlled by the interests of large tech companies would be key in driving the sector forward. I look forward to improving the user experience and integration with more machine learning tools over the next year, as well as sharing more thoughts in the space.
AIF360
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perspective off
o https://aif360.mybluemix.net/
- How to detect and tackle bias in my data?
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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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Hi Reddit! I'm Milena Pribic, Advisory Designer for AI and the global design representative for AI Ethics at IBM. Ask me anything about scaling ethical AI practices at a huge company!
My advice is to remember that bias comes into the process intentionally and unintentionally! Tools like AI Fairness 360 can help you mitigate that from a development/technical perspective: https://aif360.mybluemix.net/
- [R] What are some of the best research papers to look into for ML Bias
What are some alternatives?
fairlearn - A Python package to assess and improve fairness of machine learning models.
EthicML - Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
AIX360 - Interpretability and explainability of data and machine learning models
Jenkins - Jenkins automation server
interpret - Fit interpretable models. Explain blackbox machine learning.
model-card-toolkit - A toolkit that streamlines and automates the generation of model cards
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
clai - Command Line Artificial Intelligence or CLAI is an open-sourced project from IBM Research aimed to bring the power of AI to the command line interface.