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{\an1}We can get rid of the viscerally horrible things
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Coded Bias

{\an1}We can get rid of theviscerally horrible things {\an1}that are objectionableto our...

{\an1}We can get rid of the
viscerally horrible things {\an1}that are objectionable
to our concept {\an1}of autonomy and freedom, {\an1}like cameras that we can see
on the streets

Full Transcript

00:00:01.000 --> 00:00:03.976
{\an1}We can get rid of the viscerally horrible things
00:00:04.000 --> 00:00:05.976
{\an1}that are objectionable to our concept
00:00:06.000 --> 00:00:07.976
{\an1}of autonomy and freedom,
00:00:08.000 --> 00:00:11.000
{\an1}like cameras that we can see on the streets.
Duration
11 seconds
Views
22
Timestamp in Movie
00:32:26
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...
{\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,
You either saw, "It's election day" text - Coded Bias 00:16
You either saw, "It's election day" text