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{\an1}On internet advertising, as data scientists
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Coded Bias

{\an1}On internet advertising,as data scientists, we are competingfor eyeballs on one...

{\an1}On internet advertising,
as data scientists, we are competing
for eyeballs on one hand, {\an1}but really, we're competing
for eyeballs of rich people

Full Transcript

00:00:01.000 --> 00:00:03.976
{\an1}On internet advertising, as data scientists,
00:00:04.000 --> 00:00:07.976
we are competing for eyeballs on one hand,
00:00:08.000 --> 00:00:10.976
{\an1}but really, we're competing for eyeballs of rich people.
Duration
11 seconds
Views
18
Timestamp in Movie
00:33:10
Uploaded
May 26, 2025
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...
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,
You either saw, "It's election day" text - Coded Bias 00:16
You either saw, "It's election day" text
So by showing a slight variation just once, - Coded Bias 00:10
So by showing a slight variation just once,
{\an1}One Facebook message shown just once - Coded Bias 00:13
{\an1}One Facebook message shown just once
{\an1}Let's say that there's a politician that's promising - Coded Bias 00:22
{\an1}Let's say that there's a politician that...
{\an1}Maybe with a heavier touch they can swing - Coded Bias 00:12
{\an1}Maybe with a heavier touch they can swing