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Internet advertising as data scientists
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Coded Bias

On internet advertising as data scientists, we are competing for eyeballs on one hand,...

Self - Author, Weapons of Math Destruction: On internet advertising as data scientists, we are competing for eyeballs on one hand, but really we're competing for eyeballs of rich people. And then, the poor people, who's competing for their eyeballs? Predatory industries. So payday lenders, or for-profit colleges, or Caesars Palace. Like, really Predatory crap

Full Transcript

00:00:01.000 --> 00:00:04.016
Internet advertising as data scientists
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we are competing for eyeballs on one hand but really
00:00:08.004 --> 00:00:10.056
we're competing for eyeballs of rich people
00:00:10.056 --> 00:00:13.032
And then the poor people who's competing for their eyeballs
00:00:13.032 --> 00:00:15.002
Predatory industries
00:00:15.068 --> 00:00:18.076
so payday lenders or for profit colleges
00:00:15.068 --> 00:00:18.076
so payday lenders or for profit colleges
00:00:18.076 --> 00:00:22.056
or Caesars Palace like really predatory crap
Duration
24 seconds
Views
69
Timestamp in Movie
00:32:14
Uploaded
May 01, 2024
Genres
documentary
Production
7th Empire Media,Independent Lens,ITVS,JustFilms / Ford Foundation,Chicken & Egg Films,Women Make Movies,The Alfred P. Sloan Foundation Production Grant,Sundance Institute

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Movie Summary

When MIT Media Lab researcher Joy Buolamwini discovers that facial recognition does not see dark-skinned faces accurately, she embarks on a journey to push for the first-ever U.S. legislation against bias in algorithms that impact...
{\an1}We can get rid of the viscerally horrible things - Coded Bias 00:11
{\an1}We can get rid of the viscerally horrible things
{\an1}But the cameras that we can't see on the Internet - Coded Bias 00:13
{\an1}But the cameras that we can't see on the Internet
[gentle music] - Coded Bias 00:13
[gentle music]
{\an1}That's just the way we act as a society, - Coded Bias 00:08
{\an1}That's just the way we act as a society,
{\an1}On internet advertising, as data scientists, - Coded Bias 00:11
{\an1}On internet advertising, as data scientists,
So payday lenders or for-profit colleges - Coded Bias 00:06
So payday lenders or for-profit colleges
We have a practice on the Internet - Coded Bias 00:08
We have a practice on the Internet
[dramatic music] - Coded Bias 00:10
[dramatic music]
When you start to piece together - Coded Bias 00:10
When you start to piece together
{\an7}It's that idea that a company - Coded Bias 00:07
{\an7}It's that idea that a company
{\an7}States have tried for years - Coded Bias 00:10
{\an7}States have tried for years
{\an7}We have to think about how this might be used - Coded Bias 00:05
{\an7}We have to think about how this might be used
{\an1}- John Anderton, you could use a Guinness right about now. - Coded Bias 00:06
{\an1}- John Anderton, you could use a Guinness right about ...
- Our computers, our machine intelligence - Coded Bias 00:06
- Our computers, our machine intelligence
Machine learning is developing very rapidly, - Coded Bias 00:12
Machine learning is developing very rapidly,
[tense music] - Coded Bias 00:14
[tense music]
Maybe you have set of compulsive gamblers, - Coded Bias 00:16
Maybe you have set of compulsive gamblers,
{\an1}In the online world, - Coded Bias 00:12
{\an1}In the online world,
{\an1}Machine learning can find that person by person. - Coded Bias 00:05
{\an1}Machine learning can find that person by person.
In 2010, - Coded Bias 00:08
In 2010,