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Machine-Learning-for-High-Risk-Applications-Book

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Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

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- - README.md
  - Testing_and_Remediating_Bias_constrained.ipynb
- - Backdoor_testing.ipynb
  - Data_Poisoning.ipynb
  - README.md
  - Red_Teaming_an_XGBoost_model.ipynb
  - Training_an_Overfit_and_a_Constrained_XGBoost_model.ipynb
- - Constrained_XGB_and_Post_Hoc_Explanations.ipynb
  - GLM,GAM_and_EBM_code_example.ipynb
  - README.md
- - 1.Data Preparation.ipynb
  - 2.Transfer learning-Stage_1.ipynb
  - 3.Transfer learning-Stage_2.ipynb
  - 4.Post-Hoc Explanations.ipynb
  - 5.Adding Noise to images .ipynb
  - 6.Label_Randomization.ipynb
  - Adversarial Example Attacks.ipynb
  - README.md
  - Retraining on Gaussian Noise.ipynb
- - README.md
  - Residual_Analysis_for_XGBoost.ipynb
  - Selecting a Better XGBoost Model.ipynb
  - Sensitivity_Analysis_for_XGBoost_Adversarial_Example_Search.ipynb
  - Stress_testing_XGBoost.ipynb
- Data.zip
.gitignore
book.jpg
CONTRIBUTING.md
LICENSE.md
README.md

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# Machine-Learning-for-High-Risk-Applications-Book

This is a companion repository for the book [Machine Learning for High-Risk Applications](https://learning.oreilly.com/library/view/machine-learning-for/9781098102425/)

![Book Cover](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/blob/main/book.jpg)

<a href="https://www.amazon.com/Machine-Learning-High-Risk-Applications-Responsible/dp/1098102436">Buy on Amazon</a> |
<a href="https://learning.oreilly.com/library/view/machine-learning-for/9781098102425/">Read on O'Reilly</a>

The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.

This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
Learn how to create a successful and impactful AI risk management practice
Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
Engage with interactive resources on GitHub and Colab

## Code

The code for this book can be found in the following sections:

| Chapter | Code Notebooks |
| ------- | -------------- |
| 6       | [Explainable Boosting Machines and Explaining XGBoost](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-6) |
| 7       | [Explaining a PyTorch Image Classifier](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-7%20%26%209) |
| 8       | [Debugging XGBoost](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-8) |
| 9       | [Debugging a PyTorch Image Classifier](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-7%20%26%209) |
| 10      | [Testing and Remediating Bias with XGBoost](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-10) |
| 11      | [Red-teaming XGBoost](https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-11) |