It’s a truism that we live in a “digital age”. It would be more accurate to say that we live in an algorithmically curated era – that is, a period when many of our choices and perceptions are shaped by machine-learning algorithms that nudge us in directions favoured by those who employ the programmers who write the necessary code.
A good way of describing them would be as recommender engines. They monitor your digital trail and note what interests you – as evidenced by what you’ve browsed or purchased online. Amazon, for example, regularly offers me suggestions for items that are “based on your browsing history”. It also shows me a list of what people who purchased the item I’m considering also bought. YouTube’s engine notes what kinds of videos I have watched – and logs how much of each I have watched before clicking onwards – and then presents on the right-hand side of the screen an endlessly-scrolling list of videos that might interest me based on what I’ve just watched.
In the early days of the web, few, if any, of these engines existed. But from 2001 onwards they became increasingly common and are now almost ubiquitous. Several factors drove this expansion. One was the need to help users cope with the information overload that came with the web: recommender engines could sift through the torrent and create a personalised distillation just for you. But the prime driving force was the business model we now call surveillance capitalism – logging our online behaviour in order to make increasingly refined predictions about our preferences and likely needs that could be sold to advertisers anxious to sell us stuff.
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Source: How Amazon puts misinformation at the top of your reading list | Amazon | The Guardian