A white man between the ages of 25 and 30, weighing approximately 70kg has a superpower: the ability to represent all of humanity. For decades, humans have built and designed based on the "reference man", resulting in a massive gender data gap.
This gap impacts women's lives every day - from minor inconveniences like struggling to reach a shelf, to life-threatening situations due to ill-fitting safety kits or crashing in a car whose safety tests do not account for women. A 2017 report found that only 5% of women working in emergency services said their PPE never hampered their work. Body armours, stab vests, hi-vis vests and jackets have all been highlighted as unsuitable.
Nearly half a century after the introduction of crash test dummies, car regulators have introduced a "female" crash test dummy. In reality, this is just a scaled-down male dummy. Women are not scaled-down men. In the EU, this dummy is only required in one of five tests and only in the passenger seat. Male remains the default even today.
The data gap is evident in every tool, device, and gadget around us. The average smartphone size today is 5.5 inches. While an average man can comfortably use his device one-handed, the average woman's hand is not much bigger than the device itself.
With the advent of machine learning, the issue becomes critical. The way machine-learning works, when you feed it biased data, it gets better and better—at being biased. Being diagnosed by algorithms based on current data will make healthcare worse for women.
Speaking on her book Invisible Women, Criado Perez says that the most common excuse for exclusion of women across various fields was that women are too complex. Instead of engaging with the complexity, researchers prefer to exclude women, essentially choosing to save money rather than saving women's lives.
Written by Anushree Appandairajan
Artwork by Izzy Johns
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