AIF360
interpret
AIF360 | interpret | |
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6 | 6 | |
2,311 | 5,998 | |
1.1% | 0.5% | |
7.2 | 9.7 | |
12 days ago | 7 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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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
interpret
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[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
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[N] Google confirms DeepMind Health Streams project has been killed off
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees ๐ model) is a step in that direction https://github.com/interpretml/interpret
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[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
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[D] Generalized Additive Modelsโฆ with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
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Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
fairlearn - A Python package to assess and improve fairness of machine learning models.
shap - A game theoretic approach to explain the output of any machine learning model.
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]
shapash - ๐ Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
AIX360 - Interpretability and explainability of data and machine learning models
alibi - Algorithms for explaining machine learning models
thinc - ๐ฎ A refreshing functional take on deep learning, compatible with your favorite libraries
imodels - Interpretable ML package ๐ for concise, transparent, and accurate predictive modeling (sklearn-compatible).
model-card-toolkit - A toolkit that streamlines and automates the generation of model cards
medspacy - Library for clinical NLP with spaCy.
verifyml - Open-source toolkit to help companies implement responsible AI workflows.
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest