For those 87 million people probably wondering what was actually done with their data, I went back to Christopher Wylie, the ex-Cambridge Analytica employee who blew the whistle on the company’s problematic operations in the Observer. According to Wylie, all you need to know is a little bit about data science, a little bit about bored rich women, and a little bit about human psychology.
Step one, he says, over the phone as he scrambles to catch a train: - When you’re building an algorithm, you first need to create a training set. That is: no matter what you want to use fancy data science to discover, you first need to gather the old-fashioned way. Before you can use Facebook likes to predict a person’s psychological profile, you need to get a few hundred thousand people to do a 120-question personality quiz.
The “training set” refers, then, to that data in its entirety: the Facebook likes, the personality tests, and everything else you want to learn from. Most important, it needs to contain your “feature set”: - The underlying data that you want to make predictions on, Wylie says. - In this case, it’s Facebook data, but it could be, for example, text, like natural language, or it could be clickstream data – the complete record of your browsing activity on the web. - Those are all the features that you want to [use to] predict. At the other end, you need your target variables, the things that you’re trying to predict for. So in this case, personality traits or political orientation, or what have you.
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