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When you start to piece together
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Coded Bias

When you start to piece together {\an1}different bits of information,you start to...

When you start to piece together {\an1}different bits of information,
you start to understand someone on a very
intimate basis, {\an1}probably better than people
understand themselves

Full Transcript

00:00:01.000 --> 00:00:01.976
When you start to piece together
00:00:02.989 --> 00:00:03.976
{\an1}different bits of information, you start to understand
00:00:04.000 --> 00:00:05.976
someone on a very intimate basis,
00:00:06.000 --> 00:00:09.976
{\an1}probably better than people understand themselves.
Duration
10 seconds
Views
30
Timestamp in Movie
00:34:08
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...
{\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
[low ambient music] - Coded Bias 00:07
[low ambient music]
{\an7}I think about nine years old I saw the Media Lab on TV, - Coded Bias 00:10
{\an7}I think about nine years old I saw the Media Lab on TV...
{\an1}It could smile and move its ears in cute ways, - Coded Bias 00:13
{\an1}It could smile and move its ears in cute ways,
{\an7}I thought you kinda showed up, but here I am now. - Coded Bias 00:05
{\an7}I thought you kinda showed up, but here I am now.