Hello World: How to be Human in the Age of the Machine

by Hannah Fry

For the time being, worrying about evil AI is a bit like worrying about overcrowding on Mars.

The budget tool that wielded so much power over the residents was then handed over, and revealed to be – not some sophisticated AI, not some beautifully crafted mathematical model, but an Excel spreadsheet.

Most judges didn’t manage to make the same decision on the same case when seeing it for the second time.

Jack Stilgoe’s take on the necessary compromise: ‘Things that look like autonomous systems are actually systems in which the world is constrained to make them look autonomous.’

on the hidden dangers of relying too heavily on automated systems.48 Build a machine to improve human performance, she explained, and it will lead – ironically – to a reduction in human ability.

Here’s the problem: the chances of misidentification multiply dramatically with the number of faces in the pile. The more faces the algorithm searches through, the more chance it has of finding two faces that look similar. So, once you try using these same algorithms on bigger catalogues of faces, their accuracy plummets.

To my mind, the urgent need for algorithmic regulation is never louder or clearer than in the case of crime, where the very existence of these systems raises serious questions without easy answers.

But examples of perfectly fair, just systems aren’t exactly abundant when algorithms aren’t involved either.

what if we accepted that perfection doesn’t exist? Algorithms will make mistakes. Algorithms will be unfair. That should in no way distract us from the fight to make them more accurate and less biased wherever we can

Perhaps the answer is to build algorithms to be contestable from the ground up.

Whenever you see a story about an algorithm, see if you can swap out any of the buzzwords, like ‘machine learning’, ‘artificial intelligence’ and ‘neural network’, and swap in the word ‘magic’. Does everything still make grammatical sense?