Hello World: Being Human in the Age of Algorithms

by Hannah Fry

Machine-learning algorithms, by contrast, have recently proved to be remarkably good at tackling problems where writing a list of instructions won’t work. They can recognize objects in pictures, understand words as we speak them and translate from one language to another – something rule-based algorithms have always struggled with. The downside is that if you let a machine figure out the solution for itself, the route it takes to get there often won’t make a lot of sense to a human observer.

Although AI has come on in leaps and bounds of late, it is still only ‘intelligent’ in the narrowest sense of the word. It would probably be more useful to think of what we’ve been through as a revolution in computational statistics than a revolution in intelligence.

a paradox in our relationship with machines. While we have a tendency to over-trust anything we don’t understand, as soon as we know an algorithm can make mistakes, we also have a rather annoying habit of over-reacting and dismissing it completely, reverting instead to our own flawed judgement. It’s known to researchers as algorithm aversion. People are less tolerant of an algorithm’s mistakes than of their own – even if their own mistakes are bigger.

We’ve all called Siri an idiot at least once, somehow in the process forgetting the staggering technological accomplishment it has taken to build a talking assistant you can hold in your hand.

The outcome is biased because reality is biased. More men commit homicides, so more men will be falsely accused of having the potential to murder.

As young women, many of the now elderly nuns had been required to submit a handwritten autobiographical essay to the Sisterhood before they were allowed to take their vows. These essays were written when the women were – on average – only 22 years old, decades before any would display any symptoms of dementia. And yet, astonishingly, the scientists discovered clues in their writing that predicted what would happen to them far in the future. The researchers analysed the language in each of the essays for its complexity and found a connection between how articulate the nuns were as young women and their chances of developing dementia in old age.

If somebody has a cancer cell in their body, the chances are their immune system will identify it as a mutated cell and just attack it and kill it – that cancer will never grow into something scary. But sometimes the immune system messes up, meaning the body supports the growth of the cancer, allowing it to develop. At that point cancer can kill.

In whatever facet of life an algorithm is introduced, there will always be some kind of a balance. Between privacy and public good. Between the individual and the population. Between different challenges and priorities. It isn’t easy to find a path through the tangle of incentives, even when the clear prize of better healthcare for all is at the end. But it’s even harder when the competing incentives are hidden from view. When the benefits of an algorithm are over-stated and the risks are obscured. When you have to ask yourself what you’re being told to believe, and who stands to profit from you believing it.

If you’ve ever been broken into twice within a short space of time, you’ll be all too familiar with the boost effect. As police will tell you after you’ve first been victimized, criminals tend to repeatedly target the same location – and that means that no matter where you live, you’re most at risk in the days right after you’ve just been burgled. In fact, your chances of being targeted can increase twelvefold at this time.

‘The Soviet Union had remarkably little street crime when they were at their worst of their totalitarian, authoritarian controls. But, my God, at what price?’

Leroi doesn’t think much of the lyrical prowess of Adele either: ‘If you were to analyse any of the songs you would find no sentiment in there that couldn’t be created by a sad song generator.’