Closing this data gap is both easy and hard. It’s easy because it has a very simple solution: collect sex-disaggregated data.
But it’s hard because the gender data gap is not the product of a conspiracy by a group of misogynistic data scientists.
by Criado Perez is the author of Invisible Women: Data Bias in a World Designed for Men
For TIME Davos 202o
Illustration by Aiste Stancikatie for TIME
Did you hear the one about how aid workers rebuilt homes after a flood—and forgot to include kitchens? How about the entrepreneur whose product was dismissed by funders as too “niche”—but whose femtech company, Chiaro, is now on track for more than $100 million in 2020? Or the female sexual-dysfunction drug that was tested for its interaction with alcohol on 23 men … and only two women? Not finding any of these funny? Maybe that’s because they’re not jokes.
From cars that are 71% less safe for women than men (because they’ve been designed using a 50th-percentile male dummy), to voice-recognition technology that is 70% less likely to accurately understand women than men (because many algorithms are trained on 70% male data sets), to medication that doesn’t work when a woman is on her period (because women weren’t included in the clinical trials), we are living in a world that has been designed for men because for the most part, we haven’t been collecting data on women. This is the gender data gap. And if we want to design a world that works for the woman of the future as well as it works for the man of the present, we’re going to have to close it.
Closing this data gap is both easy and hard. It’s easy because it has a very simple solution: collect sex-disaggregated data. But it’s hard because the gender data gap is not the product of a conspiracy by a group of misogynistic data scientists. It is simply the result of an everyday bias that affects pretty much all of us: when we say human, 9 times out of 10, we mean men.
Even when we try to fix gender disparities, we still often end up using men as the default—a tendency I have christened the Henry Higgins effect, after My Fair Lady’s leading man who memorably complains, “Why can’t a woman be more like a man?” The Henry Higgins effect was visible when an executive whose voice-recognition system failed to recognize women’s voices suggested that women should undergo hours of training to fix “the many issues with women’s voices,” rather than, you know, fixing the many issues with his voice-recognition software that doesn’t recognize the voices of half the human population.
