Responsible AI: Best Practices for Creating Trustworthy AI Systems by Qinghua Lu

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Subject Area
Artificial Intelligence
Book Title
Responsible AI
ISBN-13
9780138073923
Subject
Technology
ISBN
9780138073923
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Product Identifiers

Publisher
Addison Wesley Professional
ISBN-10
0138073929
ISBN-13
9780138073923
eBay Product ID (ePID)
8064049398

Product Key Features

Number of Pages
320 Pages
Language
English
Publication Name
Responsible Ai : Best Practices for Creating Trustworthy Ai Systems
Publication Year
2023
Subject
Intelligence (Ai) & Semantics, General
Type
Textbook
Subject Area
Computers, Science
Author
Qinghua Lu, Csiro, Liming Zhu, Xiwei Xu, Jon Whittle
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Weight
18.7 Oz
Item Length
9 in
Item Width
7.4 in

Additional Product Features

Intended Audience
Scholarly & Professional
Dewey Edition
23
Dewey Decimal
174.90063
Synopsis
AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies. Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover: Governance mechanisms at industry, organisation, and team levels Development process perspectives, including software engineering best practices for AI System perspectives, including quality attributes, architecture styles, and patterns Techniques for connecting code with data and models, including key tradeoffs Principle-specific techniques for fairness, privacy, and explainability A preview of the future of responsible AI
LC Classification Number
Q334.7.L8 2024

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