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{\an1}Let's say that there's
a politician that's promising {\an1}to regulate Facebook,
and they are like, {\an1}"We are going to turn out
extra voters for your opponent." {\an1}They could do this at scale,
and you'd have no clue {\an1}because if Facebook hadn't
disclosed the 2010 experiment, {\an1}we had no idea because
it's screen by screen
a politician that's promising {\an1}to regulate Facebook,
and they are like, {\an1}"We are going to turn out
extra voters for your opponent." {\an1}They could do this at scale,
and you'd have no clue {\an1}because if Facebook hadn't
disclosed the 2010 experiment, {\an1}we had no idea because
it's screen by screen
Full Transcript
00:00:01.000 --> 00:00:02.976
{\an1}Let's say that there's
a politician that's promising
00:00:03.000 --> 00:00:05.976
{\an1}to regulate Facebook,
and they are like,
00:00:06.000 --> 00:00:08.976
{\an1}"We are going to turn out
extra voters
00:00:09.000 --> 00:00:09.977
for your opponent."
00:00:10.945 --> 00:00:14.976
{\an1}They could do this at scale,
and you'd have no clue
00:00:15.000 --> 00:00:19.976
{\an1}because if Facebook hadn't
disclosed the 2010 experiment,
00:00:20.000 --> 00:00:21.976
{\an1}we had no idea because
it's screen by screen.
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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...