The document discusses Facebook's "10 Year Challenge", where users post their profile photos from 10 years ago alongside their current photo. The author argues this could enable companies to gather a large dataset of carefully labeled before/after photos to train facial recognition algorithms. While the intent may have been harmless, it's important users are aware of how the personal data they share could be used at scale by companies. The author advocates people think carefully about what data they share and how it could potentially be accessed and used.
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Facebook's '10 Year Challenge' Is Just a Harmless Meme - Right?
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Facebook's '10 Year Challenge'
Is Just a Harmless Meme—
Right?
Alyssa Foote; Getty Images
If you use social media, you've probably noticed a trend across Facebook,
Instagram, and Twitter of people posting their then-and-now profile
pictures, mostly from 10 years ago and this year.
Instead of joining in, I posted the following semi-sarcastic tweet:
https://twitter.com/kateo/status/1084199700427927553
My flippant tweet began to pick up traction. My intent wasn't to claim that
the meme is inherently dangerous. But I knew the facial recognition
scenario was broadly plausible and indicative of a trend that people should
be aware of. It’s worth considering the depth and breadth of the personal
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data we share without reservations.
Of those who were critical of my thesis, many argued that the pictures were
already available anyway. The most common rebuttal was: “That data is
already available. Facebook's already got all the profile pictures.”
Of course they do. In various versions of the meme, people were instructed
to post their first profile picture alongside their current profile picture, or a
picture from 10 years ago alongside their current profile picture. So, yes:
These profile pictures exist, they’ve got upload time stamps, many people
have a lot of them, and for the most part they’re publicly accessible.
But let's play out this idea.
Imagine that you wanted to train a facial recognition algorithm on age-
related characteristics and, more specifically, on age progression (e.g., how
people are likely to look as they get older). Ideally, you'd want a broad and
rigorous dataset with lots of people's pictures. It would help if you knew
they were taken a fixed number of years apart—say, 10 years.
Sure, you could mine Facebook for profile pictures and look at posting
dates or EXIF data. But that whole set of profile pictures could end up
generating a lot of useless noise. People don’t reliably upload pictures in
chronological order, and it’s not uncommon for users to post pictures of
something other than themselves as a profile picture. A quick glance
through my Facebook friends’ profile pictures shows a friend’s dog who
just died, several cartoons, word images, abstract patterns, and more.
In other words, it would help if you had a clean, simple, helpfully labeled
set of then-and-now photos.
What's more, for the profile pictures on Facebook, the photo posting date
wouldn’t necessarily match the date the picture was taken. Even the EXIF
metadata on the photo wouldn't always be reliable for assessing that date.
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Why? People could have scanned offline photos. They might have uploaded
pictures multiple times over years. Some people resort to uploading
screenshots of pictures found elsewhere online. Some platforms strip EXIF
data for privacy.
Through the Facebook meme, most people have been helpfully adding that
context back in (“me in 2008 and me in 2018”) as well as further info, in
many cases, about where and how the pic was taken (“2008 at University
of Whatever, taken by Joe; 2018 visiting New City for this year’s such-and-
such event”).
In other words, thanks to this meme, there’s now a very large dataset of
carefully curated photos of people from roughly 10 years ago and now.
Of course, not all the dismissive comments in my Twitter mentions were
about the pictures being already available; some critics noted that there
was too much crap data to be usable. But data researchers and scientists
know how to account for this. As with hashtags that go viral, you can
generally place more trust in the validity of data earlier on in the trend or
campaign—before people begin to participate ironically or attempt to
hijack the hashtag for irrelevant purposes.
As for bogus pictures, image recognition algorithms are plenty
sophisticated enough to pick out a human face. If you uploaded an image
of a cat 10 years ago and now—as one of my friends did, adorably—that
particular sample would be easy to throw out.
For its part, Facebook denies having any hand in the #10YearChallenge.
"This is a user-generated meme that went viral on its own," a Facebook
spokesperson responded. "Facebook did not start this trend, and the meme
uses photos that already exist on Facebook. Facebook gains nothing from
this meme (besides reminding us of the questionable fashion trends of
2009). As a reminder, Facebook users can choose to turn facial recognition
on or off at any time.”
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But even if this particular meme isn't a case of social engineering, the past
few years have been rife with examples of social games and memes
designed to extract and collect data. Just think of the mass data extraction
of more than 70 million US Facebook users performed by Cambridge
Analytica.
Is it bad that someone could use your Facebook photos to train a facial
recognition algorithm? Not necessarily; in a way, it’s inevitable. Still, the
broader takeaway here is that we need to approach our interactions with
technology mindful of the data we generate and how it can be used at scale.
I’ll offer three plausible use cases for facial recognition: one respectable,
one mundane, and one risky.
The benign scenario: Facial recognition technology, specifically age
progression capability, could help with finding missing kids. Last year
police in New Delhi reported tracking down nearly 3,000 missing kids in
just four days using facial recognition technology. If the kids had been
missing a while, they would likely look a little different from the last known
photo of them, so a reliable age progression algorithm could be genuinely
helpful here.
Facial recognition's potential is mostly mundane: Age recognition is
probably most useful for targeted advertising. Ad displays that incorporate
cameras or sensors and can adapt their messaging for age-group
demographics (as well as other visually recognizable characteristics and
discernible contexts) will likely be commonplace before very long. That
application isn’t very exciting, but stands to make advertising more
relevant. But as that data flows downstream and becomes enmeshed with
our location tracking, response and purchase behavior, and other signals, it
could bring about some genuinely creepy interactions.
Like most emerging technology, there's a chance of fraught consequences.
Age progression could someday factor into insurance assessment and
health care. For example, if you seem to be aging faster than your cohorts,
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perhaps you’re not a very good insurance risk. You may pay more or be
denied coverage.
After Amazon introduced real-time facial recognition services in late 2016,
they began selling those services to law enforcement and government
agencies, such as the police departments in Orlando and Washington
County, Oregon. But the technology raises major privacy concerns; the
police could use the technology not only to track people who are suspected
of having committed crimes, but also people who are not committing
crimes, such as protesters and others whom the police deem a nuisance.
The American Civil Liberties Union asked Amazon to stop selling this
service. So did a portion of Amazon’s shareholders and employees, who
asked Amazon to halt the service, citing concerns for the company’s
valuation and reputation.
It's tough to overstate the fullness of how technology stands to impact
humanity. The opportunity exists for us to make it better, but to do that we
also must recognize some of the ways in which it can get worse. Once we
understand the issues, it’s up to all of us to weigh in.
So is this such a big deal? Are bad things going to happen because you
posted some already-public profile pictures to your wall? Is it dangerous to
train facial recognition algorithms for age progression and age
recognition? Not exactly.
Regardless of the origin or intent behind this meme, we must all become
savvier about the data we create and share, the access we grant to it, and
the implications for its use. If the context was a game that explicitly stated
that it was collecting pairs of then-and-now photos for age progression
research, you could choose to participate with an awareness of who was
supposed to have access to the photos and for what purpose.
The broader message, removed from the specifics of any one meme or even
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any one social platform, is that humans are the richest data sources for
most of the technology emerging in the world. We should know this, and
proceed with due diligence and sophistication.
Humans are the connective link between the physical and digital worlds.
Human interactions are the majority of what makes the Internet of Things
interesting. Our data is the fuel that makes businesses smarter and more
profitable.
We should demand that businesses treat our data with due respect, by all
means. But we also need to treat our own data with respect.
WIRED Opinion publishes pieces written by outside contributors and
represents a wide range of viewpoints. Read more opinions here. Submit
an op-ed at opinion@wired.com
Updated 1-16-19, 5:30 pm EST: This story was updated to include
comment from Facebook.
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