B1 Intermediate US 13 Folder Collection
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If you upload a photo into Google's reverse image search, it'll find websites where
that picture has appeared, or provide “visually similar images” that have the same coloring
and composition. The leading search engine in Russia, called
Yandex, has reverse image search too but it doesn't work the same way. It's not looking
for visually similar images. It's looking for similar faces, the same face.
The difference between these search engines is that Google hasn't switched on facial recognition
and Yandex has. On Google, you can enter a name and look for a face. But on Yandex, you
can enter a face and look for a name. And that distinction represents a potentially
enormous shift in our offline lives, where we usually decide who we introduce ourselves
to. Now that computer scientists have created
tools that can turn faces into nametags, it's worth reflecting on how we got here and what
we stand to lose.
A computer's facial recognition system has broadly the same components as your own facial
recognition system. You see someone with your eyes, your mind processes the features of
their face, and recalls their identity from your memory.
Now imagine if you could have eyes in lots of places and could download and store memories
from other people, then you have something more like the automated version of facial
recognition, which has only come together in the past 5 years or so.
Its eyes are digital cameras, revolutionary machines that turn light into data. "It's
a state of the art digital model, which records images on memory chips instead of photographic
film." Digital imagery arose in the early 2000s, which coincided with the arrival of
the social internet. So right when we were able to take an unlimited number of pictures,
Facebook, Flickr, Youtube, and other sites told us our images had a home online.
"100 million photos are being tagged every day on Facebook."
Professional photography also went up on websites, news articles, and photo libraries, and Google's
web crawlers gathered them into Image Search. And then the computer vision researchers went
to work. The millions of digital photos posted to the internet, like the Facebook pictures
where we tagged our friends or Google image results of celebrities-- they were used to
build the “mind” of facial recognition systems. That mind is made up of a series
of algorithms. They locate faces in an image, map facial
features to correct for head rotation, and then take over 100 measurements that define
that individual face. Those measurements are usually described as
the distance between the eyes, the length of the nose, the width of the mouth. But the
truth is, nobody knows exactly what's being measured. That's determined by a deep learning
algorithm looking for correlations in raw pixel data.
To train that algorithm, engineers give it sets of triplets: an anchor photo, another
photo of the same person, and a photo of a different person. The algorithm is tasked
with deciding what to measure so that the statistical difference between the two matching
photos is as small as possible while the distance between the non-matching photos is as large
as possible.  These algorithms are refined through millions
of examples, but they still don't perform equally well on all types of people or on
all types of photos. That hasn't stopped them from being packaged
and distributed as ready-to-use software. But whoever uses that software won't be
able to identify you until you're in their database of known faces. That's the “memory”
of the system - and it's separate from the training images.
In the case of the iphone's faceID, it's a database of one - you volunteer to store
your face on your device in exchange for easily unlocking your phone.
Companies like Facebook and Google also keep databases of their users. But it's governments
that typically have access to the largest databases of names and faces, so facial recognition
significantly expands the power of the state. They collected these images for other reasons
and now they're repurposing them for facial recognition without telling us or obtaining
our consent, which is why several US cities have banned government use of facial recognition.
Retail stores, banks, and stadiums can create or buy watchlists of known shoplifters, valued
customers, or other persons of interest, so they're notified if one of those people
shows up. And then there's another source of labeled
photos. Those are the ones we've been labeling ourselves by setting up profiles on social
media networks. It's typically against the terms of use
to program bots that can download faces and names from Linkedin, Twitter, of Facebook,
but it's doable. And what's at stake is something that most
of us take for granted: our ability to move through public spaces anonymously.
"So we typically think of public and private as being opposites. But is there such a thing
as having privacy when we're in public?" "I would like to think so."
Evan Selinger is a professor of philosophy who argues that facial recognition is a threat
to “obscurity,” Which is the idea that personal information is safer when it is hard
to obtain or understand. "So We have natural sort of limitations in
what we can perceive and what we can hear. Even the human mind has a sort of basic limits
in how much information it can store. So one of the things that technologies do
is they reduce the transaction costs of being able to find information, being
able to store information, being able to share information, and being able to correctly interpret
information. And so facial recognition is probably the most obscurity-eviscerating technology
ever invented."
We don't have to imagine how this could play out. It's already happening with photos from
the Russian social media network VK. Aric Toler, a journalist who covers Europe
for Bellingcat, showed me how it works with a random video of Russian soccer fans picking
fights in Poland. "There's about 10 or so of these soccer hooligans
in this video and for every single one of them you can find their profiles on VK. OK
I'll get this guy in the background. Let me save him. OK so here's the first result. This
guy. So if you click the photo here it will take you directly to the photo's link. And
here he is. I think he's wearing I think he's wearing the same shirt. Yeah he's wearing
the same shirt even. This is him too. So this is probably like his buddy who uploaded a
photo. Yeah. So this is this guy's profile and here's his buddy right here. Yeah so here
he is during a baptism, probably." "And the photo you uploaded is not particularly clear
or high resolution." No "not at all, right, it's just a 200 by 100. So it does feel weird
when you do this and you have access to way more information than you should, is what
it feels like. But also we only publish what we're like one thousand percent sure of
and if possible we maybe dont include the names of the people."
How you feel about this technology probably depends on how much you sympathize with the
person being identified. Bellingcat has used these tools to find identify
people linked to the attack on flight MH17 in Eastern Ukraine. It's also been used
to doxx police officers accused of brutality, anti-corruption activists protesting against
Vladimir Putin, random strangers as part of an art project, and sex workers, porn performers,
and others who have posted anonymous photos online.
"The way that we share our images and our names on social media, LinkedIn Twitter, Instagram,
it seems to suggest that we don't want to be obscure or we're not really looking to
be anonymous. Are we allowed to want to share and connect with other people online and still
be able to expect not to be recognized when we're offline in our regular lives?"
"I would say absolutely. In fact I would go further and say if we ever create a society
where that's not a reasonable expectation, a lot of the things that are fundamental to
being a human being are really going to be compromised.
Having any individuality requires experimenting in life and experimenting requires the protections
of some obscurity. But also intimacy requires obscurity. Right. If you want to be able to
share different parts of your life with different people, and I think most of us do right. We
don't want to come into work and behave the same way we do with our friends. We don't
want to treat our partners in the same way we do acquaintances. And the concern, when
you lose too much obscurity, is that these domains bleed into one another and create
what's called context collapse. And it doesn't mean that one is more real or one is more
authentic. Leading a rich life requires us to be able to express ourselves in these diverse
ways." The photos we took to share with friends,
or document history, or simply get a government ID have been used to build and operate a technology
that strips away the protections that obscurity has always provided us. It's nothing less
than a massive bait-and-switch. One that could change the meaning of the human face forever.
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What facial recognition steals from us

13 Folder Collection
Courtney Shih published on January 2, 2020
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