Hashtags and challenges have become part of internet culture – spanning across most, if not all, social media platforms. The latest trend on the Internet of Things is of course the #10yearchallenge where people share on social media two photos of themselves side by side, taken 10 years apart.
It started right around the New Year and picked up traction fairly quickly. I joined in on the fun too. After all, nostalgia is a powerful force. And besides, there’s no harm in posting another selfie, right?
Well, author and Associate Professor at UC Berkely, Kate O’Neill had a different perspective on the seemingly nonthreatening trend. After reading this, you might want to rethink joining in the fun of the #10yearchallenge.
In a series of tweet, Ms O’Neill shared a rather ominous possibility of how the #10yearchallenge provides a ripe data mining opportunity to tech companies who could use the photo comparisons to train facial recognition algorithms on age progression and age recognition.
Me 10 years ago: probably would have played along with the profile picture aging meme going around on Facebook and Instagram
Me now: ponders how all this data could be mined to train facial recognition algorithms on age progression and age recognition
— Kate O'Neill (@kateo) January 12, 2019
The original tweet garnered a lot of attention. People were either deeply disturbed by this revelation or were rather nonchalant about it. Those who were unfazed cited the fact that these photos already exist online, so it’s not like the challenge adds any extra data.
Ms O’Neill contends that just putting those two photos together – one from 10 years ago, and one current – already streamlines the data. The challenge essential provides a broad data set of photos taken a fixed number of years apart. It’s much more reliable than simply extracting data from photos posted in the past, which tend not to be chronological.
Sure, you could mine Facebook for profile pictures and look at posting dates or EXIF data. But that's a lot of noise; it'd help if you had a clean then-and-now. What's more, the photo posting date and even EXIF data wouldn't always be reliable for when the pic was actually taken.
— Kate O'Neill (@kateo) January 13, 2019
So in essence, this data provided here is actually very, very helpful if you want to train a facial recognition algorithm on age-related characteristics.
Ms O’Neill goes on to say that the potential of facial recognition technology – a reliable and well-developed one – varies. Elaborating on this in an article on Wired, she posits three uses of the technology.
The first, more benign use, is the implementation of facial recognition in age progression in finding missing children. She highlighted the case in New Delhi last year when the police reportedly tracked down nearly 3,000 missing children in a matter of days with the help of facial recognition technology. And with some of the children having been missing for a while, the age progression aspect really helped.
The other potential of facial recognition is its use in advertising. For example, using age recognition for targeted advertising by using cameras or sensors to adapt the messaging for different age-groups. While this is a less exciting scenario, this could lead to some creepy interactions.
Have you ever talked about buying something and then suddenly see ads to that exactly same thing on every webpage you visit online? Yeah, like that but worse.
Going further, Ms O’Neill suggests that age progression could eventually be factored into insurance assessment and health care. So if you seem to be aging faster than your cohorts, insurance companies might not deem you to be a good insurance risk and charge you a higher premium.
Back in 2016, Amazon has introduced real-time facial recognition services which they sold to law enforcement and government agencies. But that raised privacy concerns as the software enabled authorities to track not just people who have committed crimes but anyone else they deemed a nuisance such as protestors. I’m sure you can see what a slippery slope this can be, especially without proper oversight.
So I don’t know about you, but that certainly worries me. On the one hand, technology will advance whether you join in the #10yearschallenge or not. After all, is it estimated that by 2020 more than half the earth’s population would be connected to the Internet of Things. It’s also getting tougher to live in the modern world and stay disconnected. On the other hand, it seems Facebook now knows me better than I know myself.
Speaking of Facebook, someone suggested that they were the ones behind the #10yearschallenge, socially engineering a meme for the purposes of data mining. However, they’ve denied any involvement in the creation of that trend.
“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.”
But regardless of the origin or intent behind this meme, Ms O’Neil asserts that people need to “become savvier about the data we create and share, the access we grant to it, and the implications for its use”.
I'm not saying anyone should panic or feel bad. It's simply worth becoming more mindful of how our data can be used. We don't need to be wary of everything; we just need to think critically, and learn more about the potential our data has at scale. We're all still learning.
— Kate O'Neill (@kateo) January 13, 2019
Because as much as we should hold demand that business treat our data with respect, we should do the same.