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Gerald Sim, "Screening Big Data: Films That Shape Our Algorithmic Literacy" (Routledge, 2024)

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Inhoud geleverd door New Books Network. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door New Books Network of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.

Screening Big Data: Films that Shape Our Algorithmic Literacy (Routledge, 2024) 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 manoeuvres 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, in Screening Big Data by Dr. 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 interview was conducted by Dr. Miranda Melcher whose new book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars.

Learn more about your ad choices. Visit megaphone.fm/adchoices

Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

  continue reading

897 afleveringen

Artwork
iconDelen
 
Manage episode 443313694 series 2421470
Inhoud geleverd door New Books Network. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door New Books Network of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.

Screening Big Data: Films that Shape Our Algorithmic Literacy (Routledge, 2024) 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 manoeuvres 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, in Screening Big Data by Dr. 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 interview was conducted by Dr. Miranda Melcher whose new book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars.

Learn more about your ad choices. Visit megaphone.fm/adchoices

Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

  continue reading

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