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Machine Learning for High-Risk Applications: Techniques for Responsible AI

Machine Learning for High-Risk Applications: Techniques for Responsible AI

Machine Learning for High-Risk Applications: Techniques for Responsible AI Paperback / softback - 2023

by Patrick Hall

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Paperback / softback. New. This book describes responsible AI, a holistic approach 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.
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Details

  • Title Machine Learning for High-Risk Applications: Techniques for Responsible AI
  • Author Patrick Hall
  • Binding Paperback
  • Condition New
  • Pages 466
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2023-05-23
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # A9781098102432
  • ISBN 9781098102432 / 1098102436
  • Weight 1.7 lbs (0.77 kg)
  • Dimensions 9.13 x 6.93 x 1.1 in (23.19 x 17.60 x 2.79 cm)
  • Category Computers - General Information
  • Library of Congress subjects Risk management, Artificial intelligence
  • Dewey Decimal Code 006.31
  • Quantity available 10

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From the publisher

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

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