Artificial intelligence, far from being a rarefied utopian intervention, may turn out to be no more than the sum of its very human parts — most of them male.

That's the slightly apprehensive view of Caroline Criado Perez, author of Invisible Women: Exposing Data Bias in a World Designed for Men.

Speaking to RN's Blueprint for Living, Criado Perez made a "garbage in, gender bias out" case for the fallibility of AI.

"You would hope that artificial intelligence would improve things," she says. "Humans are so notoriously biased, you would hope that computers would be better.

"But unfortunately humans are the ones making the algorithms, and humans are the ones feeding those algorithms data: we are creating biased algorithms based on biased data."

Rather than improving the world by extracting historical gender biases, machine learning, it turns out, is further entrenching discrimination.

Whether it's voice recognition software unable to detect the female voice, or algorithms that preference male CVs, big data is introducing new forms of gender bias.

Of course, data bias is nothing new; according to Criado Perez, "most of recorded human history is one big data gap".

"The stories we tell ourselves about our past, present and future," Criado Perez writes, "are all marked — disfigured — by a female-shaped 'absent presence'."

The gender data gap has shaped the very fabric of our built world and the implications are profound.

Could snow clearing be sexist?

The unlikely but most compelling example, Criado Perez argues, is found in a small Swedish town.

A local city council was conducting a gender audit to determine whether government policies were equitable and to make sure resources were being fairly allocated (a radical exercise in itself).

Researchers decided to investigate snow clearing, at first, Criado Perez says, "as a kind of joke".

"They thought there was no way snow clearing could be sexist, what a ridiculous idea," she says. "And yet it turned out that snow clearing was in fact sexist.

"In this case it was travel patterns … typical male and female travel patterns are not the same. Men are most likely to drive — they are most likely to have a fairly simple travel pattern, just in and out of work.

"Women, because they do 75 per cent of the world's care work, do a more complicated type of travel called trip-chaining, where they are doing lots of short interconnected trips."

It's easier to drive a car through thick snow than push a stroller. ( Getty Images: Jose Luis Pelaez )

The nature of female travel patterns also means that women are more likely to take public transport and to walk.

The researchers realised snow clearing had privileged male travel patterns over female travel patterns, clearing major road arteries as a matter of priority and then the pavements. The council decided to reverse the approach.

"They figured it was easier to drive a car through three inches of snow than to push a buggy or to walk, and they figured it wouldn't cost them any money."

What they didn't realise, Criado Perez says, is that it would save them money.

By switching the snow-clearing schedule, she says, "they found that their accident and emergency costs fell dramatically, because pedestrians were dominating the numbers of people who were being admitted for having fallen and injured themselves in icy conditions and women were dominating the pedestrians."

'The implicit assumption that men are the default'

Criado Perez says the Swedish example is instructive: the snow-clearing schedule was neither designed to harm women nor prioritise men. But discrimination works instead through silence, through an absence of intention.

"[The gender data gap] is a kind of not thinking. A double not thinking, even: men go without saying, and women don't get said at all," she says.

In the context of today's data-driven world, this absence is likely to be more deadly than ever. AI is increasingly used in medical diagnoses, so what happens when that AI has been trained using biased data sets?

Medical research, Criado Perez suggests, is already systematically skewed towards men: heart attacks are routinely misdiagnosed because the "typical" heart attack symptoms are in fact only typical to men.

Similar examples in the modern world abound: from cars designed around the body of a "reference man" and crash test dummies based on the average male body to police body armour designed for men, leaving women dangerously exposed.

In the tech world, Criado Perez writes, "the implicit assumption that men are the default human remains king."

"When Apple launched its health-monitoring system … it boasted a 'comprehensive' health tracker. It could track blood pressure, steps taken, blood alcohol level," she writes. "But … they forgot one crucial detail: a period tracker."

For many women, it was an astonishing omission. For Criado Perez, it's the inevitable result of a structural imbalance.

Gender bias in an AI world is particularly disturbing, Criado Perez argues, when data is no longer simply a reflection of society, but has become its fundamental to its formation. Women make up only a small minority of programmers, software developers and tech employees.

Criado Perez argues that only the intervention and participation of women can redress the dangerous imbalance. "When we are designing a world that is meant to work for everyone, we need women in the room."