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”).
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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.
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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.
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