A curated list of awesome responsible machine learning resources.
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Updated
Oct 14, 2024
A curated list of awesome responsible machine learning resources.
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
XMLX GitHub configuration
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
An interpretable machine learning pipeline over knowledge graphs
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Demonstration of InterpretME, an interpretable machine learning pipeline
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Rule Extraction from Bayesian Networks
XMLX GitHub configuration
Default Risk Prediction from bank dataset with Interpretable Machine Learning
Overview of machine learning interpretation techniques and their implementations
H2O.ai Machine Learning Interpretability Resources
Article for Special Edition of Information: Machine Learning with Python
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
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