How algorithms create and prevent fake news : exploring the impacts of social media, deepfakes, GPT-3, and more / Noah Giansiracusa.
Giansiracusa, Noah, author.
2021
QA76.9.A43 .G53 2021
Available Online

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Title How algorithms create and prevent fake news : exploring the impacts of social media, deepfakes, GPT-3, and more / Noah Giansiracusa.
Author Giansiracusa, Noah, author.
ISBN 9781484271551 (electronic bk.)
1484271556 (electronic bk.)
9781484271544
1484271548
DOI https://doi.org/10.1007/978-1-4842-7155-1
Published [Berkeley] : Apress, [2021]
Copyright ©2021
Language English
Description 1 online resource (xii, 235 pages) : illustrations
Other Standard Identifiers 10.1007/978-1-4842-7155-1 doi
Call Number QA76.9.A43 .G53 2021
Dewey Decimal Classification 006.3/1
Summary From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias which gets amplified in harmful data feedback loops. Dont be afraid: with this book youll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed July 30, 2021).
Available in Other Form Print version: 9781484271544
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Record Appears in Online Resources > Ebooks
All Resources
1. Perils of Pageview
2. Crafted by Computer
3. Deepfake Deception
4. Autoplay the Autocrats
5. Prevarication and the Polygraph
6. Gravitating to Google
7. Avarice of Advertising
8. Social Spread
9. Tools for Truth.

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