Go on. Do that Facebook quiz. It’s probably harmless, right?
So, we’ve established that digital sucks. So, why not log on to Facebook, chat with your mates. Maybe do a few quizzes? Or answer a meme?
Just a bit of fun, right?
Now those top 10 lists are starting to give us some interesting information. Obviously we can see what genre of music you like (which, as it turns out, is a good indication of your political leanings and many other things). We also get an indication of your geographic location (based on where the artists have charted and toured), an indication of your gender, and an indicator of likely age. Some of these things we may already have from your Facebook profile, but it is always useful to have data from multiple sources, especially if you are someone who has set up your Facebook profile to be ‘mysterious’. Oh, and don’t worry if you took your kids to a boyband concert and you added that to your list, the algorithms can shuffle that noise out, and maybe even be able to pick out your children’s ages too.
It’s all too easy to forget what so many of the services we use really are, because their value to us, as consumers, is often distinct to the value delivered to advertisers or the business itself. So while Facebook might seem like a great way of communicating with friends and family — or of shooting the breeze with acquaintances — at its heart, it’s giant engine for mining information about us.
The opacity of machine learning
And machine learning techniques make it ever harder for us to spot exactly what is happening. These algorithmic approaches can make data connections that we cannot intuitively understand — and so sometimes, we’re giving away much more about ourselves than we realise:
What I have described here is a minor set of predictions, but according to a document obtained by The Australian, Facebook have been telling advertisers that they can detect when teens are “stressed”, “defeated”, “overwhelmed”, “anxious”, “nervous”, “stupid”, “silly”, “useless” and a “failure”. In other words, Facebook is able to tell advertisers when teens are at their most vulnerable. This isn’t new news, but I am still not sure many users realise quite how much they reveal in what they write on Facebook. That case is particularly sensitive since most countries have greater restrictions on how data about children can be collected and used. If this is being done for teens, then you can reasonably assume that adult data is being collected and used to at least this degree.
Now - machine learning is great in many ways. But because it’s hard for us human learners to understand, it exposes us to risks that we are ill-equipped to even suspect exists.