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{\an1}- You might use something like Snapchat filters
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Coded Bias

{\an1}- You might use somethinglike Snapchat filters {\an1}that are detecting your face...

{\an1}- You might use something
like Snapchat filters {\an1}that are detecting your face {\an1}and then putting something
onto your face

Full Transcript

00:00:01.000 --> 00:00:03.976
{\an1}- You might use something like Snapchat filters
00:00:04.000 --> 00:00:04.977
{\an1}that are detecting your face
00:00:05.003 --> 00:00:06.976
{\an1}and then putting something onto your face.
Duration
7 seconds
Views
24
Timestamp in Movie
00:26:44
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...
And then you also have algorithms - Coded Bias 00:10
And then you also have algorithms
{\an1}You can have algorithms that are trying to determine - Coded Bias 00:08
{\an1}You can have algorithms that are trying to determine
{\an7}- One of Apple's cofounders is accusing the company's - Coded Bias 00:07
{\an7}- One of Apple's cofounders is accusing the compa...
{\an7}One tech entrepreneur said - Coded Bias 00:06
{\an7}One tech entrepreneur said
{\an8}- Apple cofounder Steve Wozniak tweeted that - Coded Bias 00:10
{\an8}- Apple cofounder Steve Wozniak tweeted that
{\an8}- Really? - How do we get justice - Coded Bias 00:06
{\an8}- Really? - How do we get justice
{\an1}- Some Amazon engineers decided that they were going to use AI - Coded Bias 00:08
{\an1}- Some Amazon engineers decided that they were going t...
{\an1}- Amazon is learning a tough lesson - Coded Bias 00:05
{\an1}- Amazon is learning a tough lesson
{\an1}The company has now abandoned an AI recruiting tool - Coded Bias 00:10
{\an1}The company has now abandoned an AI recruiting tool
{\an1}- This model rejected all résumés from women. - Coded Bias 00:06
{\an1}- This model rejected all résumés from women.
{\an1}Anybody who had a women's college on their résumé, - Coded Bias 00:12
{\an1}Anybody who had a women's college on their résumé...
{\an1}There are very, very few women - Coded Bias 00:12
{\an1}There are very, very few women
{\an1}The machine was simply - Coded Bias 00:11
{\an1}The machine was simply
{\an1}They're only making decisions that are mathematical. - Coded Bias 00:05
{\an1}They're only making decisions that are mathematic...
If we use machine learning models - Coded Bias 00:09
If we use machine learning models
{\an1}It's one of the latest examples - Coded Bias 00:08
{\an1}It's one of the latest examples
[soft music] - Coded Bias 00:11
[soft music]
{\an1}The progress that was made in the Civil Rights era - Coded Bias 00:09
{\an1}The progress that was made in the Civil Rights era
[indistinct chatter] - Coded Bias 00:09
[indistinct chatter]
{\an1}Right now we have an algorithm - Coded Bias 00:07
{\an1}Right now we have an algorithm