To watch in HD, get ClipCafe PRO
Given such accuracy disparities
Please log in to add to collections.
Coded Bias

Given such accuracy disparities, I wonderedhow large tech companies {\an1}could have...

Given such accuracy disparities, I wondered
how large tech companies {\an1}could have missed these issues

Full Transcript

00:00:01.000 --> 00:00:02.976
Given such accuracy disparities,
00:00:03.000 --> 00:00:03.977
I wondered how large tech companies
00:00:04.003 --> 00:00:05.976
{\an1}could have missed these issues.
Duration
6 seconds
Views
24
Timestamp in Movie
01:15:46
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

Want This Clip in HD?

Upgrade for HD/4K downloads and unlimited access. Upgrade now →

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 harvesting of face data - Coded Bias 00:05
{\an1}The harvesting of face data
{\an1}No one should be forced to submit their face data - Coded Bias 00:09
{\an1}No one should be forced to submit their face data
{\an1}Tenants in Brooklyn are protesting the installation - Coded Bias 00:08
{\an1}Tenants in Brooklyn are protesting the installation
{\an1}There's a Big Brother Watch U.K. report - Coded Bias 00:10
{\an1}There's a Big Brother Watch U.K. report
{\an1}Our faces may well be the final frontier of privacy, - Coded Bias 00:08
{\an1}Our faces may well be the final frontier of privacy,
{\an1}Congress must act now to uphold American freedoms and rights. - Coded Bias 00:05
{\an1}Congress must act now to uphold American freedoms and ...
{\an1}- Ms. Buolamwini, I heard your opening statement, - Coded Bias 00:11
{\an1}- Ms. Buolamwini, I heard your opening statement,
- Absolutely not. - Are they most effective - Coded Bias 00:06
- Absolutely not. - Are they most effective
{\an1}- So what demographic is it mostly effective on? - Coded Bias 00:05
{\an1}- So what demographic is it mostly effective on?
{\an7}- So we have a technology - Coded Bias 00:18
{\an7}- So we have a technology
- When it comes to face recognition, - Coded Bias 00:10
- When it comes to face recognition,
{\an7}- How does the FBI get the initial database - Coded Bias 00:05
{\an7}- How does the FBI get the initial database
{\an8}I think, you know, up to 18 states - Coded Bias 00:06
{\an8}I think, you know, up to 18 states
{\an1}I guess what troubles me too, is just the fact that - Coded Bias 00:07
{\an1}I guess what troubles me too, is just the fact that
These 18 states, I think the chairman said, - Coded Bias 00:06
These 18 states, I think the chairman said,
{\an8}- China seems to me to be the dystopian path - Coded Bias 00:08
{\an8}- China seems to me to be the dystopian path
{\an1}- More than China, Facebook has 2.6 billion people, - Coded Bias 00:23
{\an1}- More than China, Facebook has 2.6 billion people,
- This is a patent that they filed, - Coded Bias 00:18
- This is a patent that they filed,
{\an1}All of our hard work, - Coded Bias 00:06
{\an1}All of our hard work,
{\an1}- I wanna show it to my mother. [laughs] - Coded Bias 00:07
{\an1}- I wanna show it to my mother. [laughs]