How to Stay Smart in a Smart World: Why Human Intelligence Still Beats Algorithms

Description

How to stay in charge in a world populated by algorithms that beat us in chess, find us romantic partners, and tell us to “turn right in 500 yards.” Doomsday prophets of technology predict that robots will take over the world, leaving humans behind in the dust. Tech industry boosters think replacing people with software might make the world a better place—while tech industry critics warn darkly about surveillance capitalism. Despite their differing views of the future, they all agree: machines will soon do everything better than humans. In How to Stay Smart in a Smart World, Gerd Gigerenzer shows why that’s not true, and tells us how we can stay in charge in a world populated by algorithms.   Machines powered by artificial intelligence are good at some things (playing chess), but not others (life-and-death decisions, or anything involving uncertainty). Gigerenzer explains why algorithms often fail at finding us romantic partners (love is not chess), why self-driving cars fall prey to the Russian Tank Fallacy, and how judges and police rely increasingly on nontransparent “black box” algorithms to predict whether a criminal defendant will reoffend or show up in court. He invokes Black Mirror, considers the privacy paradox (people want privacy, but give their data away), and explains that social media get us hooked by programming intermittent reinforcement in the form of the “like” button. We shouldn’t trust smart technology unconditionally, Gigerenzer tells us, but we shouldn’t fear it unthinkingly, either. Contents Introduction Staying on Top of Technology I The Human Affair with AI 1 Is True Love Just a Click Away? AI Finds You Love How Do Love Algorithms Work? Courtship Adapts to Software Female Bots Arranged Marriages Go Online The Ultimate Convenience: Let the AI Arrange Our Marriages 2 What AI Is Best At: The Stable-World Principle The Stable-World Principle Successes of AI Psychological AI Gaming AI The Texas Sharpshooter Fallacy The Moon Shot 3 Machines Influence How We Think about Intelligence The First Computers Were Social Systems Women Computers Intelligence Becomes Computation 4 Are Self-Driving Cars Just Down the Road? The Adapt-to-AI Principle Neural Networks in the Driver’s Seat Intuition and Morals The Future of Driving The Russian Tank Fallacy 5 Common Sense and AI The Cogs of Common Sense Language Translation Object and Face Recognition Human Errors Differ from Machine Errors Scenario Recognition Of Different Minds 6 One Data Point Can Beat Big Data Predicting the Flu Correlations and the Texas Sharpshooter Bad Big Data Big Data and Unstable Worlds Do Numbers Speak for Themselves? II High Stakes 7 Transparency Black Box Justice Transparent Justice Predictive Policing Why Algorithms Can Perpetuate Discrimination Against Uninformed Consent Why Do We Use Black Box Algorithms When We Don’t Need To? Counting Keys Wanted: The Right for Transparency in Scoring 8 Sleepwalking into Surveillance Social Credit Systems Privacy Isn’t Worth a Damn Pay with Your Money, Not with Your Privacy Sleepwalking into Surveillance Big Nudging Into the Future 9 The Psychology of Getting Users Hooked How to Get Users Hooked Is Social Media Addiction Like Gambling Addiction? 10 Safety and Self-Control Distracted Drivers Distracted Parents Use It or Lose It 11 Fact or Fake? Fakes Fairy Tales about Algorithms Personalized Ads: A Bubble about to Burst? Checking Trustworthiness The Cement of Society Acknowledgments Notes Introduction Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Bibliography Index