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So a VP from Amazon
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Coded Bias

So a VP from Amazon, in attemptingto discredit our work, {\an7}writes, "Facial analysisand...

So a VP from Amazon, in attempting
to discredit our work, {\an7}writes, "Facial analysis
and facial recognition {\an7}"are completely different {\an8}"in terms of
underlining technology and the data
used to train them." So that statement,
if you research this area, {\an1}doesn't even make sense, right?

Full Transcript

00:00:01.000 --> 00:00:02.976
So a VP from Amazon,
00:00:03.000 --> 00:00:05.976
in attempting to discredit our work,
00:00:06.000 --> 00:00:09.976
{\an7}writes, "Facial analysis and facial recognition
00:00:10.000 --> 00:00:10.977
{\an7}"are completely different
00:00:11.945 --> 00:00:13.976
{\an8}"in terms of underlining technology
00:00:14.000 --> 00:00:15.976
and the data used to train them."
00:00:16.000 --> 00:00:18.976
So that statement, if you research this area,
00:00:19.000 --> 00:00:22.976
{\an1}doesn't even make sense, right?
Duration
23 seconds
Views
26
Timestamp in Movie
00:44:29
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}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.
I, at that moment, did not realize - Coded Bias 00:07
I, at that moment, did not realize
{\an1}For example, when Americans are bet against - Coded Bias 00:11
{\an1}For example, when Americans are bet against
{\an1}So it's, like, looking for a particular profile of people - Coded Bias 00:13
{\an1}So it's, like, looking for a particular profile o...
{\an1}That was an algorithmic game that came out of Wall Street. - Coded Bias 00:05
{\an1}That was an algorithmic game that came out of Wall Str...
{\an1}The tyranny of these types of practices of discrimination - Coded Bias 00:09
{\an1}The tyranny of these types of practices of discriminat...
{\an8}- There was a world of suffering - Coded Bias 00:06
{\an8}- There was a world of suffering
{\an7}After a couple years there, I was like, - Coded Bias 00:11
{\an7}After a couple years there, I was like,