Seth Stephens-Davidowitz is a New York Times op-ed contributor, a visiting lecturer at The Wharton School, and a former Google data scientist.

He received a BA in philosophy from Stanford, where he graduated Phi Beta Kappa, and a PhD in economics from Harvard.

His research—which uses new, big data sources to uncover hidden behaviors and attitudes—has appeared in the Journal of Public Economics and other prestigious publications and has just surfaced in his new book, Everybody Lies - New Data, and What the Internet Can Tell Us About Who We Really Are, which explores what the vast amounts of information now instantly available to us reveals about ourselves and our world.

Expect to learn a number of things in this episode of Future Squared, including:

Topics Discussed:

• What kinds of things we usually tell Google that we wouldn’t tell our loved ones
• The connection between the school you go and your level of success in life
• How AI and machine learning can behave in a way that may be construed as racist
• Correlation and causation
• How companies can use data to build better products
• Why customer surveys don’t work
• What most startups get wrong about AB testing
• How much data is enough when testing assumptions underpinning a new product idea
• How your Google search history can be used to identify health risk factors and diagnose users
• Are we collecting too much data to the point where it’s difficult to extract anything meaningful?
• Are ‘online echo chambers’ are actually a thing?
• How data can be used to mitigate decisions that stem from our personal biases and entrenched belief systems
• Why the real us often shows up at 2am on PornHub

Show Notes:

Web: Sethsd.com

Twitter: @SethS_D

Amazon: https://amzn.to/2MCYvUY

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