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How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More
"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 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 occurThe history and inner-workings of YouTube’s recommendation algorithmThe 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 farThe 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.
---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 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 occurThe history and inner-workings of YouTube’s recommendation algorithmThe 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 farThe 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.
- GenresArtificial Intelligence
369 pages, Kindle Edition
Published July 14, 2021
3 people are currently reading
77 people want to read
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Displaying 1 - 2 of 2 reviews
April 18, 2022
The topic (of creating/preventing fake news) is so interesting, that I've added the book to my "A-Must-Read" list immediately after finding out about it. However, I've got something different than what I've expected ...
1. this book (even if published by Apress) is NOT technical - the descriptions of particular techniques (like GPT-3) are there, but they are very simplified and you won't learn much from them
2. the book brings in many interesting facts (no doubt about it), but very few of them are what I'd call "investigated" facts - in fact, we learn very little about what do social media actually do to combat social media; I do realize this is frequently very confidential data, but there are: leaks, speculations, observations based on the public results - etc.
3. Even the (most informative when it comes to particular tools) chapter 9 ("Tools for truth") looks surprisingly weak when compared with any good source on OSINT and/or AIML.
4. My favorite chapter was 8 ("Social Spread") which has introduced quite a nice mental model - I find it very useful..
In the end: I'd really love to learn more. More about:
- particular techniques/incidents from the past
- how actively do particular types of companies curate the content (not just social media, but also news portals or even e-commerce marketplaces)
- technical details between tools and algorithms
1. this book (even if published by Apress) is NOT technical - the descriptions of particular techniques (like GPT-3) are there, but they are very simplified and you won't learn much from them
2. the book brings in many interesting facts (no doubt about it), but very few of them are what I'd call "investigated" facts - in fact, we learn very little about what do social media actually do to combat social media; I do realize this is frequently very confidential data, but there are: leaks, speculations, observations based on the public results - etc.
3. Even the (most informative when it comes to particular tools) chapter 9 ("Tools for truth") looks surprisingly weak when compared with any good source on OSINT and/or AIML.
4. My favorite chapter was 8 ("Social Spread") which has introduced quite a nice mental model - I find it very useful..
In the end: I'd really love to learn more. More about:
- particular techniques/incidents from the past
- how actively do particular types of companies curate the content (not just social media, but also news portals or even e-commerce marketplaces)
- technical details between tools and algorithms
January 8, 2022
If you want to read a few more ways in which the world is doomed, this book takes a good look at the algorithms that are feeding us information — real news and fake news — and what we need to be doing to improve them. The author is a mathematician and the book doesn’t totally shy away from technical details, but it is very accessible and assumes no real knowledge of math or computer programming. Well worth reading!
Displaying 1 - 2 of 2 reviews