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Über dieses Buch

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.

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Perils of Pageview

The Data-Driven Economics of Online Journalism
Abstract
Much of what we know, or think we know, about what is happening in the world we learn by reading the news. But nowadays “the news” means something different than it did in generations past. What we read primarily today are articles on the internet—everything ranging from casual blog posts to meticulously researched stories on national and international news sites. The transition of journalism from print to screen does not inherently mean what we read is less truthful than it used to be. However, this technological transformation has enabled a less overt but nonetheless extraordinarily influential economic transformation: the datafication of the journalism industry. The pageviews and clicks we all sprinkle across the internet are, as I will discuss, the digital fertilizer feeding a burgeoning garden of misinformation and fake news. By tracing the financial incentives involved in the contemporary news cycle, I hope in this chapter to convey the alarming extent that data, unseen to most of us yet created by our actions and activities, is fundamentally shaping what we read every day and threatening the bulwark of traditional journalistic standards.
Noah Giansiracusa

Chapter 2. Crafted by Computer

Artificial Intelligence Now Generates Headlines, Articles, and Journalists
Abstract
Machine learning, the predominant branch of modern artificial intelligence (AI), has in recent years moved beyond the task of making data-driven predictions—it is now capable of creativity in various forms. The applications of this emerging technology are myriad; the focus in this book is the role it plays in fake news. In this chapter, you will first see examples of AI being used to create profile photos of nonexistent journalists, then AI that automatically writes headlines for articles, then AI that writes entire articles based on a user’s prompt. After exploring these examples and what they mean for the battle against disinformation, this chapter provides an accessible whirlwind tour of machine learning starting from the very beginning of the subject and leading up to the contemporary computational methods behind the synthesis of photos and text. It then concludes with a look at the AI-powered tools developed so far for automating the detection of AI-generated photos and text.
Noah Giansiracusa

Chapter 3. Deepfake Deception

What to Trust When Seeing Is No Longer Believing
Abstract
The deep learning generative adversarial networks (GANs) discussed in the previous chapter for creating synthetic photos have also been applied to video synthesis and editing. Clips can now be created of people doing and saying things they never did or said in real life. This is leading to a double-pronged challenge in society’s attempts at discerning the truth: fake videos are spreading across the internet causing people to believe in events that never took place, and simultaneously real videos have been falsely claimed as deepfakes causing people to doubt reality itself. In this chapter, you will see how deepfake videos have impacted politics and journalism, how the discord they sow relates to that of previous generations of image and video manipulation, and what legal and technological attempts are being made to rein them in.
Noah Giansiracusa

Chapter 4. Autoplay the Autocrats

The Algorithm and Politics of YouTube Recommendations
Abstract
As trust in traditional media outlets has declined, people have turned to alternative sources to get their news. One particularly popular platform in this regard, especially among the younger generations (as you’ll soon see in this chapter with some precise facts and figures), is YouTube. The premise that anyone can post videos showing or explaining what is happening in the world is appealing, but the reality is that YouTube has played an alarming role in the spread of fake news and disinformation. The powerful yet mysterious YouTube recommendation algorithm drives the majority of watch time on the site, so understanding how it works is crucial to understanding how YouTube has pushed viewers toward outlandish conspiracy theories and dangerous alt-right provocateurs. This chapter takes a close look at how the recommendation algorithm has developed over the years, how it behaves in practice, how it may have influenced elections and political events around the world, how the company has responded to criticism, and how it has tried to moderate the content it hosts.
Noah Giansiracusa

Chapter 5. Prevarication and the Polygraph

Can Computers Detect Lies?
Abstract
Wouldn’t it be nice if we could take a video clip of someone talking and apply AI to determine whether or not they’re telling the truth? Such a tool would have myriad applications, including helping in the fight against fake news: a dissembling politician giving a dishonest speech would immediately be outed, as would a conspiracy theorist knowingly posting lies on YouTube. With the remarkable progress in deep learning in recent years, why can’t we just train an algorithm by showing it lots of videos of lies and videos of truth and have it learn which is which based on whatever visual and auditory clues it can find? In fact, for the past fifteen years, people have been trying this 21st-century algorithmic reinvention of the polygraph. How well it works and what it has been used for are the main questions explored in this chapter. To save you some suspense: this approach would create almost as much fake news as it would prevent—and claims to the contrary by the various companies involved in this effort are, for lack of a better term, fake news. But first, I’ll start with the fascinating history of the traditional polygraph to properly set the stage for its AI-powered contemporary counterpart.
Noah Giansiracusa

Chapter 6. Gravitating to Google

The Dangers of Letting an Algorithm Answer Our Questions
Abstract
Billions of people turn to Google to find information, but there is no guarantee that what you find there is accurate. As awareness of fake news has risen in recent years, so has the pressure on Google to find ways of modifying its algorithms so that trustworthy content rises to the top. Fake news is not limited to Google’s main web search platform—deceptive and harmful content also play a role on other Google products such as Google Maps, Google News, and Google Images, and it also shows up on Google’s autocomplete tool that feeds into all these different products. In this chapter, I’ll look at the role fake news plays in all these contexts and what Google has done about it over the years. In doing so, I’ll take a somewhat more expansive view of the term “fake news” compared with previous chapters to include hateful racist stereotypes and bigoted misinformation.
Noah Giansiracusa

Chapter 7. Avarice of Advertising

How Algorithmic Ad Distribution Funds Fake News and Reinforces Racism
Abstract
When we think of Google supporting the fake news industry, the first thing that comes to mind is how, as described in the previous chapter, it serves up an audience with its various search products. However, there is an entirely separate way—less obvious but extremely influential—that Google supports the fake news industry: financially through ad revenue. The first half of this chapter focuses on the mechanics and scale of Google’s algorithmic ad distribution system, the extent to which it funds fake news organizations, and the reluctant steps Google has taken over the years to curtail this dangerous flow of funds.
Noah Giansiracusa

Chapter 8. Social Spread

Moderating Misinformation on Facebook and Twitter
Abstract
In this chapter, I explore several ways in which algorithms interact with the complex dynamics of social media when it comes to fake news. First, I set the stage with some context, issues, and examples that help us better understand what has happened and what’s at stake. Next, I look at how algorithms have been used to scrape data from social media platforms to provide remarkable quantitative insight into how fake news spreads—both organically and when part of deliberate disinformation campaigns. Along the way, the role in this spread played by the social media platforms’ own content recommendation algorithms is explored. Attention is then turned to the algorithmic tools that social media companies—primarily Facebook and Twitter—have used and could potentially use in their battle against harmful misinformation, as well as the limitations and challenges of taking algorithmic approaches to this thorny, multifaceted problem.
Noah Giansiracusa

Chapter 9. Tools for Truth

Fact-Checking Resources for Journalists and You
Abstract
This chapter starts by discussing a collection of publicly available (and mostly free) fact-checking tools that are powered by machine learning; this is to help you know what kinds of tools are available and to provide some insight into how they work. It concludes with a brief look at the role and scope of fact-checking at Google, YouTube, Facebook, and Twitter.
Noah Giansiracusa

Backmatter

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