healthyR.ai
AIF360
Our great sponsors
| healthyR.ai | AIF360 | |
|---|---|---|
| 1 | 6 | |
| 13 | 2,311 | |
| - | 2.3% | |
| 5.7 | 7.2 | |
| 3 months ago | 9 days ago | |
| R | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
healthyR.ai
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Boilerplate XGBoost function with {healthyR.ai}
Code to the function in the post: https://github.com/spsanderson/healthyR.ai/blob/HEAD/R/boilerplate-xgboost.R
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.
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]
AIX360 - Interpretability and explainability of data and machine learning models
interpret - Fit interpretable models. Explain blackbox machine learning.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
model-card-toolkit - A toolkit that streamlines and automates the generation of model cards
verifyml - Open-source toolkit to help companies implement responsible AI workflows.
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.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Jenkins - Jenkins automation server
EthicML - Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
fairness - It's about Fairness. Supporting the CULT Family https://github.com/orgs/cultfamily-on-github/repositories