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Title: GitHub - jphall663/interpretable_machine_learning_with_python: Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

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Description: Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. - jphall663/interpretable_machine_learning_with_python

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https://patch-diff.githubusercontent.com/jphall663/interpretable_machine_learning_with_python#further-reading
Machine Learning: Considerations for fairly and transparently expanding access to credithttp://info.h2o.ai/rs/644-PKX-778/images/Machine%20Learning%20-%20Considerations%20for%20Fairly%20and%20Transparently%20Expanding%20Access%20to%20Credit.pdf
A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testinghttps://www.mdpi.com/2078-2489/11/3/137
An Introduction to Machine Learning Interpretability, 2nd Editionhttps://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf
On the Art and Science of Explainable Machine Learninghttps://arxiv.org/pdf/1810.02909.pdf
Proposals for model vulnerability and securityhttps://www.oreilly.com/ideas/proposals-for-model-vulnerability-and-security
Proposed Guidelines for the Responsible Use of Explainable Machine Learninghttps://arxiv.org/pdf/1906.03533.pdf
Real-World Strategies for Model Debugginghttps://medium.com/@jphall_22520/strategies-for-model-debugging-aa822f1097ce
Warning Signs: Security and Privacy in an Age of Machine Learninghttps://fpf.org/wp-content/uploads/2019/09/FPF_WarningSigns_Report.pdf
Why you should care about debugging machine learning modelshttps://www.oreilly.com/radar/why-you-should-care-about-debugging-machine-learning-models/
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