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{\an1}I wanted to create something
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Coded Bias

{\an1}I wanted to create something for people who wereoutside of the tech world

{\an1}I wanted to create something for people who were
outside of the tech world

Full Transcript

00:00:01.000 --> 00:00:03.976
{\an1}I wanted to create something
00:00:04.000 --> 00:00:06.000
for people who were outside of the tech world.
Duration
6 seconds
Views
22
Timestamp in Movie
00:39:00
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...
I'm excited about what it could do, - Coded Bias 00:09
I'm excited about what it could do,
{\an1}[soft dramatic music] - Coded Bias 00:17
{\an1}[soft dramatic music]
There were some vulnerabilities - Coded Bias 00:18
There were some vulnerabilities
{\an7}- I [bleep] hate feminists, - Coded Bias 00:05
{\an7}- I [bleep] hate feminists,
{\an7}- It did not take long for internet trolls - Coded Bias 00:06
{\an7}- It did not take long for internet trolls
{\an7}- It's important to note it's not the movie - Coded Bias 00:05
{\an7}- It's important to note it's not the movie
{\an7}These were human beings training them, - Coded Bias 00:07
{\an7}These were human beings training them,
{\an1}- Microsoft shut Tay off - Coded Bias 00:12
{\an1}- Microsoft shut Tay off
{\an1}Many companies utilize me to optimize their tasks. - Coded Bias 00:06
{\an1}Many companies utilize me to optimize their tasks.
I am listening. I am learning. - Coded Bias 00:06
I am listening. I am learning.
{\an1}I am making predictions for your life right now. - Coded Bias 00:05
{\an1}I am making predictions for your life right now.
{\an1}[thrumming tense music] - Coded Bias 00:11
{\an1}[thrumming tense music]
Turns out Amazon, like all of its peers, - Coded Bias 00:10
Turns out Amazon, like all of its peers,
- Introducing Amazon Rekognition Video, - Coded Bias 00:11
- Introducing Amazon Rekognition Video,
{\an1}Recognize and track persons of interest - Coded Bias 00:07
{\an1}Recognize and track persons of interest
{\an1}- When our research came out, "The New York Times" - Coded Bias 00:07
{\an1}- When our research came out, "The New York Times...
{\an1}And the headline reads, - Coded Bias 00:25
{\an1}And the headline reads,
- People were like, "How did you even, like, - Coded Bias 00:07
- People were like, "How did you even, like,
{\an1}Like, I did... I wasn't able to use a lot of, like, - Coded Bias 00:15
{\an1}Like, I did... I wasn't able to use a lot of, lik...
{\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...