Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado-Perez combines two of my favourite things: data and feminism. It reminded me a lot of Inferior by Angela Saini, in that it was infuriating.
The book has compiled loads of examples where the lack of data leads to discrimination, illness and even death for women affected. But the thing that is absolutely staggering isn’t that the data shows discrimination, but rather that most most the time, the data is just not there. I’ve come across some of this before, for example that most medical data isn’t based on actual research with women, because pesky hormones make it more difficult to study. But seriously, it’s 50% of the friggin’ population, how is this still allowed to happen in 2019?
Some of the other really interesting parts where specific case studies, such as clearing snow in Sweden or playgrounds in Vienna. I love where experimentation and actual data overturned long held assumptions. Equally, it was just mind-boggling that women were just excluded from so many things that affected them – for example in post-disaster areas where they didn’t build houses with kitchens. How do you not build houses without kitchens? More distressing was how women are more likely to die in disasters because of not having the kit to deal with pregnant women or that they are less likely to be alerted to dangers like cyclones if they live in a society were women can’t go out without a male chaperone. It’s really quite horrifying.
And even bloody tax!
Basically, it was great, but made you really angry about literally everything in the world. The most aggravating part is that even though there are some laws/resolutions that state that women should be included in the data or when researching, they are largely being ignored.
The only thing that I thought could be better was basically impossible. I wanted the data that showed the data was missing, but that’s basically not there. It’s hard to be systematic when there simply isn’t the data available. I would love to read an equivalent book in about 20 years time and see if things have improved. I really friggin’ hope so, but given some of the examples I’m not exactly hopeful.
Books like this make me reaffirm the reason why I read so many women authors, because there’s already an implicit bias against them in so many ways. And I love stories that women write so I’m going to spend my money on them instead of their male colleagues (most of the time anyway!)
This was such an important book to write. I really hope all professions start to pick up on the fundamental brokenness of their datasets. And if you imagine how awful data for women is, can you imagine what it’s like for anyone else who isn’t white, male and able-bodied. DESPAIR.
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