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How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media Deepfakes GPT-3 and More by Noah Giansiracusa

How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media Deepfakes GPT-3 and More

by Noah Giansiracusa
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Book cover type: Paperback
  • ISBN13: 9781484271544
  • Binding: Paperback
  • Subject: Computer Science and Information Technology
  • Publisher: Springer Verlag
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 235
  • Original Price: EUR 44.99
  • Language: English
  • Edition: N/A
  • Item Weight: 354 grams
  • BISAC Subject(s): Programming / Algorithms, Ethics & Moral Philosophy, and Artificial Intelligence / General

From the Back Cover

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. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.

Noah Giansiracusa received a PhD in mathematics from Brown University and is an Assistant Professor of Mathematics and Data Science at Bentley University, a business school near Boston. He previously taught at U.C. Berkeley, University of Georgia, and Swarthmore College. He has dozens of publications in math and data science and has taught courses ranging from a first-year seminar on quantitative literacy to graduate machine learning. Most recently, he created an interdisciplinary seminar on truth and lies in data that was the impetus for this book. He has received national grants and spoken at international conferences for his research in mathematics, and he has been quoted several times in Forbes as an expert on artificial intelligence. Noah also created a high school outreach program for underrepresented and disadvantaged youths, focusing on mathematics and statistics in the courtroom, that was headlined by an Obama-appointed Federal Circuit judge.

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