Databite No. 119: Mary L. Gray

Title:
Databite No. 119: Mary L. Gray
Description:

This talk by anthropologist Mary L. Gray is based on her latest book, "Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass," a collaboration with computer scientist Siddharth Suri. Ghost Work is a necessary and revelatory exposé of the invisible human workforce that powers the web—and that foreshadows the true future of work.

Hidden beneath the surface of the web, lost in our wrong-headed debates about AI, a new menace is looming. This book unveils how services delivered by companies like Amazon, Google, Microsoft, and Uber can only function smoothly thanks to the judgment and experience of a vast, invisible human labor force. These people doing “ghost work” make the internet seem smart. They perform high-tech piecework: flagging X-rated content, proofreading, designing engine parts, and much more. An estimated 8 percent of Americans have worked at least once in this “ghost economy,” and that number is growing. They usually earn less than legal minimums for traditional work, they have no health benefits, and they can be fired at any time for any reason, or none.

There are no labor laws to govern this kind of work, and these latter-day assembly lines draw in—and all too often overwork and underpay—a surprisingly diverse range of workers: harried young mothers, professionals forced into early retirement, recent grads who can’t get a toehold on the traditional employment ladder, and minorities shut out of the jobs they want. Gray and Suri also show how ghost workers, employers, and society at large can ensure that this new kind of work creates opportunity—rather than misery—for those who do it.

Amara.org co-founder Dean Jansen join Mary in a conversation moderated by Data & Society’s Director of Research Sareeta Amrute.

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Video Language:
English
Team:
Volunteer
Duration:
01:10:35
http://www.youtube.com/watch?v=zj2DEQCOTh0
Format: Youtube
Primary
Original
Added   by Judit @Amara
Format: Youtube
Primary
Original