Patrick Hall James Curtis Parul Machine Learning for High-Risk Appli (Paperback)

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
Country of Origin
US
Book Title
Machine Learning for High-Risk Applications
EAN
9781098102432
ISBN
9781098102432
Genre
Computing & Internet
ISBN-10
1098102436
Edition
1st
Publication Name
Machine Learning for High-Risk Applications
Subtitle
Approaches to Responsible AI
Release Date
05/02/2023
Release Year
2023
Category

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1098102436
ISBN-13
9781098102432
eBay Product ID (ePID)
25057238456

Product Key Features

Number of Pages
466 Pages
Publication Name
Machine Learning for High-Risk Applications : Approaches to Responsible Ai
Language
English
Publication Year
2023
Subject
Software Development & Engineering / General, Security / Online Safety & Privacy, Intelligence (Ai) & Semantics, Computer Science, Data Processing, Software Development & Engineering / Systems Analysis & Design, Social Aspects / Human-Computer Interaction
Type
Textbook
Subject Area
Computers
Author
Parul Pandey, James Curtis, Patrick Hall
Format
Trade Paperback

Dimensions

Item Height
1.2 in
Item Weight
28.4 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

LCCN
2023-280039
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
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
LC Classification Number
Q325.5.H3 2023

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