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{\an1}Like, I did... I wasn't able to use a lot of, like
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Coded Bias

{\an1}Like, I did... I wasn't ableto use a lot of, like, {\an1}open source facial...

{\an1}Like, I did... I wasn't able
to use a lot of, like, {\an1}open source facial recognition
software and stuff, {\an1}so we were sort of like,
"Hey, this is, like, someone that finally
is recognizing the problem {\an1}and trying to address it
academically." {\an1}- You can go race something

Full Transcript

00:00:01.000 --> 00:00:03.976
{\an1}Like, I did... I wasn't able to use a lot of, like,
00:00:04.000 --> 00:00:05.976
{\an1}open source facial recognition software and stuff,
00:00:06.000 --> 00:00:07.976
{\an1}so we were sort of like, "Hey, this is, like,
00:00:08.000 --> 00:00:09.976
someone that finally is recognizing the problem
00:00:10.000 --> 00:00:12.976
{\an1}and trying to address it academically."
00:00:13.000 --> 00:00:14.976
{\an1}- You can go race something.
Duration
15 seconds
Views
29
Timestamp in Movie
00:42:40
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}The lead author of the paper, who is somebody that I mentor, - Coded Bias 00:09
{\an1}The lead author of the paper, who is somebody that I m...
[dramatic music] - Coded Bias 00:10
[dramatic music]
{\an1}Amazon has been under fire for the use of - Coded Bias 00:12
{\an1}Amazon has been under fire for the use of
{\an1}So Amazon trialing their AI technology with the FBI. - Coded Bias 00:05
{\an1}So Amazon trialing their AI technology with the FBI.
{\an1}If they knowingly sold systems - Coded Bias 00:10
{\an1}If they knowingly sold systems
[siren wailing] - Coded Bias 00:17
[siren wailing]
So a VP from Amazon, - Coded Bias 00:23
So a VP from Amazon,
{\an1}like, I remember he wrote, - Coded Bias 00:08
{\an1}like, I remember he wrote,
{\an1}- I was gonna say... I was, like, - Coded Bias 00:07
{\an1}- I was gonna say... I was, like,
It wasn't a well thought out piece, - Coded Bias 00:09
It wasn't a well thought out piece,
{\an1}- I don't know if you guys feel this way, - Coded Bias 00:06
{\an1}- I don't know if you guys feel this way,
It's a continuation of the experiences - Coded Bias 00:07
It's a continuation of the experiences
{\an1}Expect your research to be dismissed. - Coded Bias 00:05
{\an1}Expect your research to be dismissed.
[melancholy music] - Coded Bias 00:24
[melancholy music]
So for me, it was disconcerting, - Coded Bias 00:12
So for me, it was disconcerting,
- When I was 14, I went to a math camp - Coded Bias 00:19
- When I was 14, I went to a math camp
{\an1}Like, I remember being told - Coded Bias 00:23
{\an1}Like, I remember being told
{\an8}It was 2006, - Coded Bias 00:08
{\an8}It was 2006,
{\an1}I didn't know anything about programming - Coded Bias 00:06
{\an1}I didn't know anything about programming
{\an1}When I first got there, I kind of drank the Kool-Aid. - Coded Bias 00:05
{\an1}When I first got there, I kind of drank the Kool-Aid.