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  <titleInfo>
    <title>Screening big data</title>
    <subTitle>films that shape our algorithmic literacy</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Sim, Gerald</namePart>
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    <dateIssued encoding="marc">2025</dateIssued>
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  <abstract>"This book examines the influence of key films on public understanding of big data and the algorithmic systems that structure our digitally mediated lives. From star-powered blockbusters to civic-minded documentaries positioned to facilitate weighty debates about artificial intelligence, these texts frame our discourse and mediate our relationship to technology. Above all, they impact society's abilities to regulate AI and navigate Big Tech's political and economic maneuvers to achieve market dominance and regulatory capture. Foregrounding data politics with close readings of key films like Moneyball, Minority Report, The Social Dilemma, and Coded Bias, Gerald Sim reveals compelling ways in which films and tech industry-adjacent media define apprehension of AI. With the mid-2010s techlash in danger of fizzling out, Screening Big Data explores the relationship between this resistance and cultural infrastructure while highlighting the urgent need to refocus attention onto how technocentric media occupy the public imagination. This book will interest students and scholars of film and media studies, digital culture, critical data studies, and technopolitics"--</abstract>
  <note type="statement of responsibility">Gerald Sim.</note>
  <subject authority="lcsh">
    <topic>Technology in motion pictures</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Big data in motion pictures</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Artificial intelligence in motion pictures</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Motion pictures</topic>
    <topic>Social aspects</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>SOCIAL SCIENCE / Media Studies</topic>
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  <classification authority="lcc">PN1995.9.T43</classification>
  <identifier type="isbn">9781003171201</identifier>
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  <identifier type="isbn">1040102654</identifier>
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  <identifier type="uri">https://www.taylorfrancis.com/books/9781003171201</identifier>
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