![]() What's more, in the EU, this dummy is required in just one of five regulatory tests, and only in the passenger seat. In reality, this dummy is simply a scaled-down male dummy-and women are not scaled-down men. In the last few years, car regulators seem to have belatedly realized that women exist, and have since introduced what they call a “female” car crash test dummy. In the 1950s, when car crash test dummies were first introduced, the dummies were based around the 50th-percentile male, as if he were the 50th-percentile human. In the 1930s, the influential Swiss architect Le Corbusier came up with “the human scale” for architecture, and by "human" he meant a six-foot man with his arm raised. ![]() What does the default male body look like? And how are you able to determine if something was designed for a “default male” body? Men (and women, and people of all gender identities) obviously come in different shapes and sizes. You mention the “default male” in the book as being the body type for which so many things are designed, from seat belts to cell phones. And so tech for women remains, for the most part, of the shrink it, pink it and price it up variety, rather than genuinely catering to women’s needs. ![]() They are therefore less likely to get funding. As a result, entrepreneurs developing new tech for women need good data because they will not be able to rely on a VC already having personal experience with, for example, how terrible all the current breast pump options are.īut because we lack data on female bodies, such entrepreneurs are less likely to have the information they need to make the case for their ideas. Ninety-three percent of venture capitalists are men, and these teams suffer from the same problem as male-dominated developer teams: they simply aren't aware of certain female needs. Tech is littered with examples of how this plays out, from Apple’s “comprehensive” health app that you could use to track your copper intake but not your period, to step tracker apps that forget women usually don’t have pockets big enough to carry their phones on them at all times. And so the tech that they develop will inevitably be biased towards white middle-class men from America. This is a data gap in its own way: white middle-class men from America simply cannot be aware of the needs of all of humanity. The other issue is how male-dominated tech is. If they had designed their schedule based on sex-disaggregated travel and hospital admission data in the first place, they could have saved a lot of money over the decades. Because it wasn’t men in their cars who were falling over and fracturing their bones: it was women pushing buggies through the snow. ![]() So they decided to switch the order around-and found to their surprise that the number of admissions to the emergency room fell dramatically. As a result, the order in which the snow was being cleared (major roads first local roads and sidewalks second) benefitted men. They make lots of short interconnected trips, and are more likely to use public transport. But because women have to combine their paid work with their unpaid care work (women still do 75 percent of the world’s unpaid care work), their travel patterns are more complicated. Men tend to have much simpler travel patterns than women: a twice daily commute in a car. When local officials in the town of Karlskoga in Sweden looked at their snow-clearing schedules, they realized that they had designed them to meet the needs of men. How, exactly, did you notice this showing up in your research? Can you give us an example that really surprised you? In the book, which cites hundreds of studies from around the world, you talk about how women are systematically excluded from data gathering.
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