MIT.Press.-.Auditing.AI.2026.Retail.EPUB.eBook-BitBook

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2026-05-21

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 ■▀▀▀   ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀  ░ BitBook Presents ░  ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀■

                                   Auditing AI                                  
                        by The Marquand House Collective                        
                                 (c) MIT Press                                  

 ■▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄   ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄   ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄■
                        ▀▄▀                          ▀▄▀
   DATE: 21-05-2026      ▓   SIZE: 1.42 MB            ▓   PAGES: n/a           
   FORMAT: EPUB          ▒   DISKS: 1x50.00MB         ▒   EDITION: n/a          
   LANGUAGE: English     ░   ISBN: 9780262051736      ░   REMOVED: DRM

https://www.worldcat.org/search?q=bn%3A9780262051736&fq=x0%3Abook+%2B+x4%3Adigit

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  How tech companies, journalists, and policymakers can prevent AI decision-
 making from going wrong. Our lives are increasingly governed by automated
 systems influencing everything from medical care to policing to employment
 opportunities, but researchers and investigative journalists have proven that
 AI systems regularly get things wrong. Auditing AI is a first-of-its-kind
 exploration of why and how to audit artificial intelligence systems. It
 offers a simple roadmap for using AI audits to make product and policy
 changes that benefit companies and the public alike. The book aims to
 convince readers that AI systems should be subject to robust audits to
 protect all of us from the dangers of these systems. Readers will come away
 with an understanding of what an AI audit is, why AI audits are important,
 key components of an audit that follows best practices, how to interpret an
 audit, and the available choices to act on an audit's results. The book is
 organized around canonical examples: from AI-powered drones mistakenly
 targeting civilians in conflict areas to false arrests triggered by facial
 recognition systems that misidentified people with dark skin tones to HR
 hiring software that prefers men. It explains these definitive cases of AI
 decision-making gone wrong and then highlights specific audits that have led
 to concrete changes in government policy and corporate practice. The Marquand
 House Collective: Marc Aidinoff, Lena Armstrong, Esha Bhandari, Ellery
 Roberts Biddle, Motahhare Eslami, Karrie Karahalios, Nate Matias, DanaΘ
 Metaxa, Alondra Nelson, Christian Sandvig, and Kristen Vaccaro.
 
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