Название: Machine Learning for High-Risk Applications: Approaches to Responsible AI (Final)Автор: Patrick Hall, James Curtis, Parul Pandey
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 469
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 42.7 MB
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.
Performance testing alone is not enough in machine learning, where very different models can have the same performance due to model multiplicity. Models must also be explainable, secure, and fair. This is the first book that emphasizes inherently interpretable models and their recent development and application, particularly in cases where models impact individuals, such as in consumer finance. In these scenarios, where explainability standards and regulations are particularly stringent, the explainable AI (XAI) post hoc explainability approach often faces significant challenges.
Developing reliable and safe machine learning systems also requires a rigorous evaluation of model weaknesses. This book presents two thorough examples alongside a methodology for model debugging, including identifying model flaws through error or residual slicing, evaluating model robustness under input corruption, assessing the reliability or uncertainty of model outputs, and testing model resilience under distribution shift through stress testing. These are crucial topics for developing and deploying Machine Learning in high-risk settings.
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
Contents:
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