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*[*A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing*](https://www.mdpi.com/2078-2489/11/3/137)
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*[*An Introduction to Machine Learning Interpretability, 2nd Edition*](https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf)
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*[*On the Art and Science of Explainable Machine Learning*](https://arxiv.org/pdf/1810.02909.pdf)
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*[*Proposals for model vulnerability and security*](https://www.oreilly.com/ideas/proposals-for-model-vulnerability-and-security)
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