Gender Disparities
Invisible Women

Gender Disparities

Human rights activist Caroline Criado Perez, OBE, offers a detailed critique – which she backs with numerous studies – of how male norms permeate society to the detriment of women.

Although information rules today’s world, data sources and analysis are not gender-balanced. Human rights activist Caroline Criado Perez explains that the gap in data about women and the “male default” in data analysis produce devastating consequences in women’s lives, such as UK doctors misdiagnosing 50% of women having heart attacks.

Perez offers a feminist manifesto for the tech age, a suggested feminist revolution in data. She supports her findings with numerous, detailed real-world examples and studies from the United States, United Kingdom and other nations. Perez’s wake-up call to data scientists, social scientists and policymakers reveals the real-world effects of not collecting and analyzing data about women. 

The Financial Times named this Book of the Year, and Perez won the McKinsey Business Book of the Year Award and The Royal Society Science Book Prize. Publishers Weekly got right to the point, calling this, “A provocative, vital book.” The Times (UK) described it as “…a game-changer; an uncompromising blitz of facts, sad, mad, bad and funny, making an unanswerable case and doing so brilliantly…the ambition and scope and sheer originalityis huge.”

Gender-Specific Data

Today, artificial intelligence (AI) supplements medical diagnoses as big data becomes the norm in health care. But half the population suffers as a result because information on heart attacks or child car constraints – and other health issues – derives, according to Perez, solely from data about men.

The introduction of big data into a world full of gender data gaps can magnify and accelerate already-existing discriminations.Caroline Criado Perez

This leads to simple annoyances like office temperatures set to accommodate the resting male temperature, which is often too cold for women. This lack of aggregated data also can lead, for example, to personal protection equipment (PPE) that fails to protect female emergency responders. A Trades Union Congress (TUC) study found that 95% of female first responders said the gear affected their ability to do their job.

In the United Kingdom, Perez reports, half of female heart attack victims suffer incorrect diagnoses. Women having heart attacks often experience trouble breathing, queasiness, exhaustion and stomach pain, but no chest pain. This may clarify why 75% of the patients at the UK’s National Health Service (NHS) special heart attack centers are male. Criteria for an emergency procedure and for access to the centers requires the chest-grabbing symptoms many women don’t experience, especially younger ones.

Human Data Default to Male Data

Historical and cultural norms, Perez explains, can incorrectly define objectivity in data. In most information gathering, men’s lives are taken to represent humanity overall. Most algorithms and AI focus on data sets with huge, but seemingly undetected, gender gaps.

Perez cites a 2014 Scientific American opinion piece contending that running medication trials with men and women wastes resources. The concept of biological similarity endures, despite men and women’s differing reactions to diseases and drugs.

We have to start recognizing that the work women do is not an added extra: Women’s work, paid and unpaid, is the backbone of our society and our economy. It’s about time we started valuing it.Caroline Criado Perez

The obvious elephant in the room is, Perez notes, is menstruation. Inclusion of women in drug trials usually occurs early in their cycles when their hormones are lowest – that is, when women are most similar to men. But, Perez says, female patients often must take various medications throughout their menstrual cycles, often resulting in too much medication or too little. Supposedly inclusive health tracking applications don’t include women’s cycles.

Adverse prescription drug reactions (ADRs) are more common among women than men. Most drug doses derive from a male-default model, and that puts women at risk of overdoses. The assumption of gender sameness is dangerous because of women’s usual higher body-fat percentage, more blood flow in body fat tissue, lower base metabolic rate, less bile acid and slower kidney filtering.

Oblivious Thinking.

The author tells of supposedly “gender-neutral” products that are problematic for women. For example, Perez writes, piano keyboards have a 7.4-inch span per octave, an issue for 87% of female pianists. Female pianists have almost double the risk of pain and injury as their male peers.

Similarly, Perez reports that a female official in Philadelphia found developers putting kitchens on the third floor of apartment buildings that had no elevators, a problem for women carrying groceries – or strollers. She offers this in contrast to Viennese officials who carefully defined the needs of public housing residents, mostly women. The “sex-disaggregated” data they collected resulted in buildings with a central courtyard and centralized kitchens.

Technology created to make life safer, such as voice recognition software, is generally built for men. When the female owner of a 2012 Ford Focus found that voice commands did not work while she was driving, customer service reps told her, “it wasn’t ever going to work” for her, due to the difference in the pitch of her voice compared to a man’s.

Blind Auditions

In the 1950s and 1960s, the New York Philharmonic Orchestra occasionally included a woman, but in the 1970s the number of women in the orchestra increased. Today, women make up 45% of the New York Philharmonic Orchestra. The change came about when a discrimination lawsuit triggered blind auditions – concealing auditioning musicians behind a curtain as they play, so the judges won’t know their gender but will only hear their music.

Perez describes an analysis of 248 US tech companies’ performance reviews showing that managers’ evaluations of female employees featured “personality criticism,” including the word “aggressive.” In men’s reviews, that word appeared only twice. If companies examine such data and act to improve their approach, Perez believes, the situation can change.

Women’s Unpaid Work

Globally, Perez reveals, women do 75% of unpaid work, such as housekeeping, child care and elder care. Numerous studies connect stress and depression to the hours in a workweek, but they don’t account for the unpaid work many women do after work. Caregiving affects many women’s paid careers as they change hours or jobs to accommodate child or elder care. In the United Kingdom, women make up 75% of part-time workers because those jobs give them flexibility. In the United States, 40 million unpaid workers care for sick and elderly relatives. 

Husbands create an extra seven hours of housework a week for wives. (University of Michigan)Caroline Criado Perez

Perez found that women scientists did double the unpaid household work their male counterparts performed. An Australian study found that single men and women perform similar levels of unpaid work. But when a man and woman live together, Perez relates, no matter their jobs, the woman’s unpaid work grows and the man’s declines.

Straightforward Activism

Queen Elizabeth awarded Perez an OBE for her human rights work, and she’s won numerous other accolades for her tireless zeal. That may lead you to think she is a partisan polemicist. Instead, she writes as a daily journalist, laying out facts to make her arguments in unadorned, rational prose. Though she passionately believes in her causes, Perez leaves the passion off to the page to enable readers to draw their own conclusions about the injustices, engrained institutional ignorance and willful blindness she reports. Though the studies Perez cites are from a great variety of sources, her thoughtful curation brings her message home with great force.

Related worthy works include Data Feminism by Catherine D’Ignazio and Lauren F. Klein; Weapons of Math Destruction by Cathy O’Neil; Automating Inequality by Virginia Eubanks; and Algorithms of Oppression by Safiya Umoja Noble.

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