Machine Learning for High-Risk Applications: Approaches to Responsible AI (Record no. 98174)

MARC details
000 -LEADER
fixed length control field 02020 a2200205 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231222b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355429728
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter HAL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Hall Patrick
245 ## - TITLE STATEMENT
Title Machine Learning for High-Risk Applications: Approaches to Responsible AI
250 ## - EDITION STATEMENT
Edition statement 1st
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff/O'Reilly
Date of publication, distribution, etc 2023
Place of publication, distribution, etc India
300 ## - PHYSICAL DESCRIPTION
Extent 464
520 ## - Remark
Summary, etc 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.<br/><br/>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.<br/><br/>Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security<br/>Learn how to create a successful and impactful AI risk management practice<br/>Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework<br/>Engage with interactive resources on GitHub and Colab<br/><br/>Source: https://www.amazon.in/Machine-Learning-High-Risk-Applications-Responsible/dp/935542972X/ref=sr_1_1?crid=SRP4D2OO6XAE&keywords=9789355429728&qid=1703250702&sprefix=9789355424150%2Caps%2C796&sr=8-1
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Curtis James
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Pandey Parul
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a Business Analytics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Collection Type Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 06/12/2023 Shroff Publisher 1440.00 Indian Book 005.74015 HAL 119107 06/12/2023 1800.00 Book

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