It's a joy to read a book by a mathematician who knows how to write. [...] There is no better
guide to the strategies and stakes of this battle for the future. ---Paul Romer Nobel Laureate
University Professor in Economics at NYU and former Chief Economist at the World Bank. By
explaining the flaws and foibles of everything from Google search to QAnon-and by providing
level-headed evaluations of efforts to fix them-Noah Giansiracusa offers the perfect starting
point for anyone entering the maze of modern digital media. -Jonathan Rauch senior fellow at
the Brookings Institute and contributing editor of The Atlantic 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
todayis 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. What You Will Learn The ways that data
labeling and storage impact machine learning and how feedback loops can occur The history and
inner-workings of YouTube's recommendation algorithm The state-of-the-art capabilities of
AI-powered text generation (GPT-3) and video synthesis doctoring (deepfakes) and how these
technologies have been used so far The algorithmic tools available to help with automated
fact-checking and truth-detection Who This Book is For People who don't have a technical
background (in data computers etc.) but who would like to learn how algorithms impact society
business leaders who want to know the powers and perils of relying on artificial intelligence.
A secondary audience is people with a technical background who want to explore the larger
social and societal impact of their work.
