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When it comes to face recognition
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Coded Bias

When it comesto face recognition, {\an1}the FBI has not fully testedthe accuracy {\an1}of...

When it comes
to face recognition, {\an1}the FBI has not fully tested
the accuracy {\an1}of the systems it uses,
yet the agency is now {\an1}reportedly piloting Amazon's
face recognition product

Full Transcript

00:00:01.000 --> 00:00:01.977
- When it comes to face recognition,
00:00:02.989 --> 00:00:03.976
{\an1}the FBI has not fully tested the accuracy
00:00:04.000 --> 00:00:06.976
{\an1}of the systems it uses, yet the agency is now
00:00:07.000 --> 00:00:09.976
{\an1}reportedly piloting Amazon's face recognition product.
Duration
10 seconds
Views
30
Timestamp in Movie
01:17:17
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}- How does the FBI get the initial database - Coded Bias 00:05
{\an7}- How does the FBI get the initial database
{\an8}I think, you know, up to 18 states - Coded Bias 00:06
{\an8}I think, you know, up to 18 states
{\an1}I guess what troubles me too, is just the fact that - Coded Bias 00:07
{\an1}I guess what troubles me too, is just the fact that
These 18 states, I think the chairman said, - Coded Bias 00:06
These 18 states, I think the chairman said,
{\an8}- China seems to me to be the dystopian path - Coded Bias 00:08
{\an8}- China seems to me to be the dystopian path
{\an1}- More than China, Facebook has 2.6 billion people, - Coded Bias 00:23
{\an1}- More than China, Facebook has 2.6 billion people,
- 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,