Machine Learning for High-Risk Applications

Patrick Hall

ISBN 10: 1098102436 / ISBN 13: 9781098102432

Published by O'reilly Media Jun 2023, 2023

About this Item

Neuware -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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 438 pp. Englisch.

Seller Inventory # 9781098102432

Bibliographic Details

• Title: Machine Learning for High-Risk Applications
• Publisher: O'reilly Media Jun 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 1098102436
• ISBN 13: 9781098102432
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 177H x 26D millimeters 231 millimeters width by 177 millimeters height by 26 millimeters depth
• Weight: 818 grams

About this title

Synopsis

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

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.