BY THE SAME AUTHOR
Do it Like a Woman
Copyright © 2019 Caroline Criado Perez
Jacket © 2019 Abrams
Published in 2019 by Abrams Press, an imprint of ABRAMS.
All rights reserved. No portion of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, electronic, photocopying, recording, or otherwise, without written permission from the publisher.
Library of Congress Control Number: 2018936302
ISBN: 978-1-4197-2907-2
eISBN: 978-1-68335-314-0
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For the women who persist: keep on being bloody difficult
Contents
Preface
Introduction: The Default Male
Part I: Daily Life
Chapter 1: Can Snow-Clearing be Sexist?
Chapter 2: Gender Neutral With Urinals
Part II: The Workplace
Chapter 3: The Long Friday
Chapter 4: The Myth of Meritocracy
Chapter 5: The Henry Higgins Effect
Chapter 6: Being Worth Less Than a Shoe
Part III: Design
Chapter 7: The Plough Hypothesis
Chapter 8: One-Size-Fits-Men
Chapter 9: A Sea of Dudes
Part IV: Going to the Doctor
Chapter 10: The Drugs Don’t Work
Chapter 11: Yentl Syndrome
Part V: Public Life
Chapter 12: A Costless Resource to Exploit
Chapter 13: From Purse to Wallet
Chapter 14: Women’s Rights are Human Rights
Part VI: When it Goes Wrong
Chapter 15: Who Will Rebuild?
Chapter 16: It’s Not the Disaster that Kills You
Afterword
Acknowledgements
Endnotes
Index
Representation of the world, like the world itself, is the work of men; they describe it from their own point of view, which they confuse with the absolute truth.
Simone de Beauvoir
Preface
Most of recorded human history is one big data gap. Starting with the theory of Man the Hunter, the chroniclers of the past have left little space for women’s role in the evolution of humanity, whether cultural or biological. Instead, the lives of men have been taken to represent those of humans overall. When it comes to the lives of the other half of humanity, there is often nothing but silence.
And these silences are everywhere. Our entire culture is riddled with them. Films, news, literature, science, city planning, economics. The stories we tell ourselves about our past, present and future. They are all marked – disfigured – by a female-shaped ‘absent presence’. This is the gender data gap.
The gender data gap isn’t just about silence. These silences, these gaps, have consequences. They impact on women’s lives every day. The impact can be relatively minor. Shivering in offices set to a male temperature norm, for example, or struggling to reach a top shelf set at a male height norm. Irritating, certainly. Unjust, undoubtedly.
But not life-threatening. Not like crashing in a car whose safety measures don’t account for women’s measurements. Not like having your heart attack go undiagnosed because your symptoms are deemed ‘atypical’. For these women, the consequences of living in a world built around male data can be deadly.
One of the most important things to say about the gender data gap is that it is not generally malicious, or even deliberate. Quite the opposite. It is simply the product of a way of thinking that has been around for millennia and is therefore a kind of not thinking. A double not thinking, even: men go without saying, and women don’t get said at all. Because when we say human, on the whole, we mean man.
This is not a new observation. Simone de Beauvoir made it most famously when in 1949 she wrote, ‘humanity is male and man defines woman not in herself, but as relative to him; she is not regarded as an autonomous being. [. . .] He is the Subject, he is the Absolute – she is the Other.’1 What is new is the context in which women continue to be ‘the Other’. And that context is a world increasingly reliant on and in thrall to data. Big Data. Which in turn is panned for Big Truths by Big Algorithms, using Big Computers. But when your big data is corrupted by big silences, the truths you get are half-truths, at best. And often, for women, they aren’t true at all. As computer scientists themselves say: ‘Garbage in, garbage out.’
This new context makes the need to close the gender data gap ever more urgent. Artificial intelligence that helps doctors with diagnoses, that scans through CVs, even that conducts interviews with potential job applicants, is already common. But AIs have been trained on data sets that are riddled with data gaps – and because algorithms are often protected as proprietary software, we can’t even examine whether these gaps have been taken into account. On the available evidence, however, it certainly doesn’t look as if they have.
Numbers, technology, algorithms, all of these are crucial to the story of Invisible Women. But they only tell half the story. Data is just another word for information, and information has many sources. Statistics are a kind of information, yes, but so is human experience. And so I will argue that when we are designing a world that is meant to work for everyone we need women in the room. If the people taking decisions that affect all of us are all white, able-bodied men (nine times out of ten from America), that too constitutes a data gap – in the same way that not collecting information on female bodies in medical research is a data gap. And as I will show, failing to include the perspective of women is a huge driver of an unintended male bias that attempts (often in good faith) to pass itself off as ‘gender neutral’. This is what de Beauvoir meant when she said that men confuse their own point of view with the absolute truth.
The female-specific concerns that men fail to factor in cover a wide variety
