Hello World: Being Human in the Age of Algorithms

by Hannah Fry

did have a truly revolutionary approach to medicine. He believed that the causes of disease were to be understood through rational investigation, not magic. By placing his emphasis on case reporting and observation, he established medicine as a science, justly earning himself a reputation as ‘the father of modern medicine’.

Take fifteenth-century China, when healers first realized they could inoculate people against smallpox. After centuries of experimentation, they found a pattern that they could exploit to reduce the risk of death from this illness by a factor of ten. All they had to do was find an individual with a mild case of the disease, harvest their scabs, dry them, crush them and blow them into the nose of a healthy person.

Evidence exists that primitive vaccinations for the bovine form small pox existed in Anatolia (Letters from Istanbul, Lady Montegue, the wife of the British Ambassador)

Ignaz Semmelweis,

one 2015 study took 72 biopsies of breast tissue, all of which were deemed to contain cells with benign abnormalities (a category towards the middle of the spectrum) and asked 115 pathologists for their opinion. Worryingly, the pathologists only came to the same diagnosis 48 per cent of the time.

you could work all of these in as extra instructions, running through every single possible type of dog ear, or dog fur, or sitting position, but your algorithm will soon become so enormous it’ll be entirely unworkable, before you’ve even begun to distinguish dogs from other four-legged furry creatures. You need to find another way. The trick is to shift away from the rule-based paradigm and use something called a ‘neural network’.11

The surprising thing about neural networks is that their operators usually don’t understand how or why the algorithm reaches its conclusions.

the operators only know that they’ve tuned up their algorithm to get the answers right; they don’t necessarily know the precise details of how it gets there.

On what basis then, one can improve the precision (specificity, positive predictive value) of an algorithm?

‘deep learning’.

Geoffrey Hinton

A specific test will have hardly any false positives, while a sensitive one has few false negatives.

In 2013, Harvard researchers secretly hid an image of a gorilla in a series of chest scans and asked 24 unsuspecting radiologists to check the images for signs of cancer. Eighty-three per cent of them failed to notice the gorilla, despite eye-tracking showing that the majority were literally looking right at it.15 Try it yourself with the picture above.16

The Nun Study In 1986, an epidemiologist from the University of Kentucky named David Snowden managed to persuade 678 nuns to give him their brains.

Ninety per cent of the nuns who went on to develop Alzheimer’s had ‘low linguistic ability’ as young women, while only 13 per cent of the nuns who maintained cognitive ability into old age got a ‘low idea density’ score in their essays.

I am not convinced of a relationship here. Sometimes, it is no more than a coincidence.

only around one in ten ‘in situ’ cancers will turn into something that could kill you. None the less, a quarter of women who receive this diagnosis in the United States will undergo a full mastectomy

Digital diagnosis

Google Brain team, who have built an algorithm that screens for the world’s biggest cause of preventable blindness – diabetic retinopathy.

Doctors call this differential diagnosis. Mathematicians call it Bayesian inference.

the UK’s National Health Service doesn’t link its healthcare records together as a matter of standard practice.

A machine like Watson could help save any number of Tamaras, but it’ll only be able to find patterns in the data if that data is collected, collated and connected.

There is a stark contrast between the rich and detailed datasets owned by data brokers and the sparse and disconnected datasets found in healthcare.

Timandra Harkness, author of Big Data: Does Size Matter?

Francis Galton was a Victorian statistician, a human geneticist, one of the most remarkable men of his generation – and the half-cousin of Charles Darwin. Many of his ideas had a profound effect on modern science, not least his work that essentially laid the foundations for modern statistics. For that, we owe Galton a sincere debt of gratitude. (Unfortunately, he was also active in the burgeoning eugenics movement, for which we certainly do not.)

whetted

voracious

in 1884, when a huge exhibition was held in London under the patronage of Queen Victoria to celebrate the advances Britain had made in healthcare, Galton saw his opportunity. At his own expense, he set up a stand at the exhibition – he called it an ‘Anthropometric Laboratory’ – in the hopes of finding a few people among the millions of visitors who’d want to pay money to be measured.

Punters

thr...

Fast-forward 130 years and you might just recognize some similarities in the current trend for genetic testing. For the bargain price of £149, you can send off a saliva sample to the genomics and biotechnology company 23andMe in exchange for a report of your genetic traits,

The company, meanwhile, has cleverly amassed a gigantic database of genetic information, now stretching into the millions of samples. It’s just what the internet giants have been doing, except in handing over our DNA as part of the trade, we’re offering up the most personal data we have. The result is a database we all stand to benefit from.

‘The long game here is not to make money selling kits, although the kits are essential to get the base level data.’ Something worth remembering whenever you send off for a commercial genetic report: you’re not using the product; you are the product.

facetious.

fraught

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.

Adam Rutherford

A Brief History of Everyone Who Ever Lived: The Stories in Our Genes (London: Weidenfeld & Nicolson, 2016).

tumbleweeds

‘Should your driverless car hit a pedestrian to save your life?’

‘What happens to roadkill or traffic tickets when our vehicles are in control?’

British researchers tried adapting a Citroën DS19 to communicate with the road in the 1960s

tarmac.’

LiDAR (Light Detection and Ranging,

fire out a photon from a laser, time how long it takes to bounce off an obstacle and come back, and end up with a good estimate of how far away that obstacle is.

LiDAR can’t help with texture...

blue circle on Google Maps that surrounds your location – it’s there to indicate the potential error in the GPS reading. Sometimes the blue circle will be small and accurately mark your position; at other times it will cover a much larger area and be centred on entirely the wrong place.

a way to make sensible guesses in a messy world. It all comes down to a phenomenally powerful mathematical formula, known as Bayes’ theorem.

This is all Bayes’ theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence.

Sharon Bertsch McGrayne, author of The Theory That Would Not Die:

‘Bayes runs counter to the deeply held conviction that modern science requires objectivity and precision.’31 By providing a mechanism to measure your belief in something, Bayes allows you to draw sensible conclusions from sketchy observations, from messy, incomplete and approximate data – even from ignorance.

Bayes is a powerful tool for distilling and understanding what we really know.

Thomas Bayes, the British Presbyterian minister and talented mathematician after whom the theorem is named. Back in the mid-1700s, he wrote an essay which included details of a game he’d devised to explain the problem. It went something a little like this:32 Imagine you’re sitting with your back to a square table. Without you seeing, I throw a red ball on to the table. Your job is to guess where it landed. It’s not going to be easy: with no information to go on, there’s no real way of knowing where on the table it could be. So, to help your guess, I throw a second ball of a different colour on to the same table. Your job is still to determine the location of the first ball, the red one, but this time I’ll tell you where the second ball ends up on the table relative to the first: whether it’s in front, behind, to the left or right of the red ball. And you get to update your guess. Then we repeat. I throw a third, a fourth, a fifth ball on to the table, and every time I’ll tell you where each one lands relative to the very first red one – the one whose position you’re trying to guess. The more balls I throw and the more information I give you, the clearer the picture of the red ball’s position should become in your mind. You’ll never be absolutely sure of exactly where it sits, but you can keep updating your belief about its position until you end up with an answer you’re confident in.