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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.
About the Author: Patrick Hall is principal scientist at bnh.ai, a Cc.C.-based law firm focused on AI and data analytics, and visiting faculty at the George Washington University School of Business (GWSB). James Curtis is a quantitative researcher focused on US power markets and renewable resource asset management. Parul Pandey is a Machine Learning Engineer at Weights & Biases.
Title: Machine Learning for High-Risk Applications:...
Publisher: O'Reilly Media
Publication Date: 2023
Binding: Soft cover
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