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{\an8}- Apple cofounder Steve Wozniak tweeted that
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Coded Bias

{\an8}- Apple cofounderSteve Wozniak tweeted that {\an7}he got ten times the creditlimit...

{\an8}- Apple cofounder
Steve Wozniak tweeted that {\an7}he got ten times the credit
limit his wife received {\an7}even though they have
no separate accounts {\an8}or separate assets

Full Transcript

00:00:01.000 --> 00:00:02.976
{\an8}- Apple cofounder Steve Wozniak tweeted that
00:00:03.000 --> 00:00:05.976
{\an7}he got ten times the credit limit his wife received
00:00:06.000 --> 00:00:07.976
{\an7}even though they have no separate accounts
00:00:08.000 --> 00:00:09.976
{\an8}or separate assets.
Duration
10 seconds
Views
25
Timestamp in Movie
00:27:17
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...
{\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
{\an1}If we're not checking, that algorithm could actually - Coded Bias 00:11
{\an1}If we're not checking, that algorithm could actua...
{\an8}[Claude Debussy's "Clair de lune"] - Coded Bias 00:14
{\an8}[Claude Debussy's "Clair de lune"]
{\an1}So the name of our organization is Big Brother Watch, - Coded Bias 00:11
{\an1}So the name of our organization is Big Brother Watch,
{\an1}"You had to live... did live, - Coded Bias 00:07
{\an1}"You had to live... did live,
{\an1}"in the assumption that every sound you made was overheard, - Coded Bias 00:09
{\an1}"in the assumption that every sound you made was ...