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下面是一个局部的机器学习蓝图，可以从总体上帮助降低做机器学习任务的风险程度。 
▌资料目录
全面的软件示例和教程（Comprehensive Software Examples and Tutorials） 可解释性或合适的增强软件包（Explainability- or Fairness-Enhancing Software Package） 免费的书（Free Books） 其他可解释性和合适的资源和列表 论文（Review and General Papers） 教学资源（Teaching Resources） 可解释（“白盒”）或合适的建模包（Interpretable ('Whitebox') or Fair Modeling Packages）
▌Comprehensive Software Examples and Tutorials
Getting a Window into your Black Box Model IML Interpretable Machine Learning with Python Interpreting Machine Learning Models with the iml Package Machine Learning Explainability by Kaggle Learn Model Interpretability with DALEX Model Interpretation series by Dipanjan (DJ) Sarkar: The Importance of Human Interpretable Machine Learning Model Interpretation Strategies Hands-on Machine Learning Model Interpretation
Partial Dependence Plots in R Visualizing ML Models with LIME
▌Expalinability-or Fairness-Enhancing Software Packages
Browser Python R ALEPlot breakDown DALEX ExplainPrediction featureImportance forestmodel fscaret ICEbox iml lightgbmExplainer lime live mcr pdp shapleyR smbinning vip xgboostExplainer
▌Free Books
Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models Fairness and Machine Learning Interpretable Machine Learning
▌Other Interpretability and Fairness Resources and Lists
8 Principles of Responsible ML An Introduction to Machine Learning Interpretability Awesome interpretable machine learning ;) Awesome machine learning operations algoaware criticalML Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship Machine Learning Ethics References Machine Learning Interpretability Resources MIT AI Ethics Reading Group XAI Resources
▌Review and General Papers
A Comparative Study of Fairness-Enhancing Interventions in Machine Learning A Survey Of Methods For Explaining Black Box Models Challenges for Transparency Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning On the Art and Science of Machine Learning Explanations On the Responsibility of Technologists: A Prologue and Primer Please Stop Explaining Black Box Models for High-Stakes Decisions The Mythos of Model Interpretability The Promise and Peril of Human Evaluation for Model Interpretability Towards A Rigorous Science of Interpretable Machine Learning Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
▌Review and General Papers
An Introduction to Data Ethics
Fairness in Machine Learning Human-Center Machine Learning Practical Model Interpretability
▌Interpretable('Whitebox') or Fair Modeing Packages
参考链接：https://github.com/jphall663/awesome-machine-learning-interpretability#comprehensive-software-examples-and-tutorials https://github.com/h2oai/mli-resources END-
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