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{\an1}- More than China, Facebook has 2.6 billion people
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Coded Bias

{\an1}- More than China, Facebookhas 2.6 billion people, {\an1}so Facebook has a...

{\an1}- More than China, Facebook
has 2.6 billion people, {\an1}so Facebook has a patent
where they say, {\an1}"Because we have all
of these face prints, {\an1}"we can now give you
an option as a retailer to identify somebody
who walks into the store." {\an1}And in their patent they say, {\an1}"We can also give that face
a trustworthiness score." {\an1}- Facebook is selling this now?

Full Transcript

00:00:01.000 --> 00:00:04.976
{\an1}- More than China, Facebook has 2.6 billion people,
00:00:05.000 --> 00:00:06.976
{\an1}so Facebook has a patent where they say,
00:00:07.000 --> 00:00:08.976
{\an1}"Because we have all of these face prints,
00:00:09.000 --> 00:00:12.976
{\an1}"we can now give you an option as a retailer
00:00:13.000 --> 00:00:15.976
to identify somebody who walks into the store."
00:00:16.000 --> 00:00:16.977
{\an1}And in their patent they say,
00:00:17.012 --> 00:00:21.976
{\an1}"We can also give that face a trustworthiness score."
00:00:22.000 --> 00:00:22.977
{\an1}- Facebook is selling this now?
Duration
23 seconds
Views
28
Timestamp in Movie
01:18:03
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...
- This is a patent that they filed, - Coded Bias 00:18
- This is a patent that they filed,
{\an1}All of our hard work, - Coded Bias 00:06
{\an1}All of our hard work,
{\an1}- I wanna show it to my mother. [laughs] - Coded Bias 00:07
{\an1}- I wanna show it to my mother. [laughs]
{\an8}♪♪ - Coded Bias 00:10
{\an8}♪♪
{\an8}So we have a... [audio cuts out] - Coded Bias 00:07
{\an8}So we have a... [audio cuts out]
{\an7}- So what demographic is it mostly effective on? - Coded Bias 00:05
{\an7}- So what demographic is it mostly effective on?
{\an7}And who are the primary engineers and designers - Coded Bias 00:06
{\an7}And who are the primary engineers and designers
{\an7}- Somerville, Massachusetts, - Coded Bias 00:05
{\an7}- Somerville, Massachusetts,
{\an8}- Oakland becomes the third major city to ban - Coded Bias 00:10
{\an8}- Oakland becomes the third major city to ban
{\an1}- At our last tenants' town hall meeting, - Coded Bias 00:13
{\an1}- At our last tenants' town hall meeting,
- But the thing is, that doesn't mean - Coded Bias 00:05
- But the thing is, that doesn't mean
{\an1}We not only educated ourselves about facial recognition, - Coded Bias 00:08
{\an1}We not only educated ourselves about facial recognitio...
{\an1}And if we can ban it in the state, - Coded Bias 00:07
{\an1}And if we can ban it in the state,
{\an1}- Well, I will say, even though the battle is ongoing, - Coded Bias 00:13
{\an1}- Well, I will say, even though the battle is ongoing,
{\an1}- "To the Brooklyn tenants - Coded Bias 00:18
{\an1}- "To the Brooklyn tenants
{\an1}The victory is ours." - Coded Bias 00:09
{\an1}The victory is ours."
- [laughs] - Coded Bias 00:08
- [laughs]
{\an1}What it means to be human is to be vulnerable. - Coded Bias 00:06
{\an1}What it means to be human is to be vulnerable.
{\an1}Being vulnerable, there's more of a capacity for empathy. - Coded Bias 00:05
{\an1}Being vulnerable, there's more of a capacity for ...
{\an1}There's more of a capacity for compassion. - Coded Bias 00:05
{\an1}There's more of a capacity for compassion.