Why Gender Equality In The Workplace Is Not Enough

Most organizations recognize the benefits of having a gender diverse workplace in the modern economy—equal hiring practices lead to higher engagement, more creativity, and better talent recruitment. But are there benefits that go beyond equal numbers of men, women, and gender-diverse people in the room?

Londa Schiebinger, a Professor of History of Science at Stanford and Director of the Gender Innovations in Science, Health & Medicine, Engineering, and Environment Project, has a unique take on this question.  She points out that in business and industry, “gender variables” are all about spurring creativity and innovation; but in science, gender variables can mean the difference between life and death. Her research has led to the development of numerous case studies highlighting innovation gaps due to lack of gender insight.  She cites three of these examples:

data-param-cid="62cec241-7d09-4462-afc2-f72f8d8ef40a"
data-player-id="44f947fb-a5ce-41f1-a4fc-78dcf31c262a"
data-playlist-id=3e5e03f9-7925-4400-8f37-b4daede06b7f
data-elements-player="true"
layout="responsive"
width="16"
height="9"
>

Seat belts: Seat belts are not optimized to fit pregnant women. The three-point seat belt, developed in the 1950s and still in use today rides up on the pregnant belly, putting the fetus and mother at risk. As a result, motor vehicle crashes continue to be a leading cause of fetal death related to maternal trauma. What is needed is a seat belt for pregnant women.

Heart disease detection: Heart disease is the number one killer of U.S. women. Because heart disease was long considered primarily a male disease, coronary angiography, the “gold standard” for diagnosing patients with chest pain, was developed to detect the typical pattern of male disease. Unfortunately, the technology is blind to the disease in a large proportion of women (especially young women).  As a result, women are often mis- and under-diagnosed. Recognizing that men and women havedifferent patterns and symptoms of heart disease can enhance diagnoses and treatment.

Algorithms: Google translate has a masculine default. Articles about Schiebinger herself are translated (from Spanish, a pro-drop language) as “he said,” “he wrote,” and, occasionally, “it thought.” It’s not just Google, numerous algorithms, when trained on historical data, inherit the bias in the system. This means that past bias is perpetuated into the future, even when governments, universities, and companies (like Google) themselves have implemented policies to foster equality. The big question is how can humans intervene in automated systems to create society we want?

How do technological oversights like these happen?  In her book called Has Feminism Changed Science?, Schiebinger explains that many research and development teams are homogenous, which may inadvertently lead to biases in what is researched and how the research is carried out. Men and women have different social experiences and often different insights and priorities when it comes to research. In fact, it wasn’t until more women became professors of history that questions surrounding the history of birth control and sanitary products began to be asked.

Schiebinger’s groundbreaking research led to a new field of study called “gendered innovations,” which utilizes methods of sex and gender analysis for innovation and discovery. As a Professor of the History of Science at Stanford, she collaborates with researchers medicine, computer science, and engineering, helping to integrate social insights into technical work. She also educates students who often become leaders in these fields. In addition, she works with funding agencies and peer-reviewed journal editors to ensure that sex and gender analysis are properly integrated into the design of research. She believes it is essential that we understand how unconscious biases inform research and innovation so we as a society can create technologies that meet the needs of complex and diverse user groups.

In Schiebinger’s mind, the problem goes beyond the numbers of women in the room to tech itself that does not always work for women. Her goal is to debug and debias tech through emphasizing the need to enhance teams, methods, and research topics. Universities and industry need to be open to how incorporating sex/gender analysis when envisioning new technologies can spark creativity, offer new perspectives, and open new areas to research. Schiebinger’s message is a simple one. Companies are not likely to achieve gender equality in numbers of men and women until we overhaul how products are produced. Schiebinger likes to emphasize three “fixes”: “fix the numbers of women,” “fix the institutions,” and “fix the knowledge.” Most people think the first step is to fix the numbers of women, she says, but in reality, it is only when we change the culture that women’s voices are heard.  When we open up our ideas about what true excellence in technology is, then will we being to create seat belts for pregnant women, medical devices that “see” women’s heart disease as well as men’s, and algorithms for fairness. When we accomplish this, women will flood into the field. We need all three fixes, and we need them now, Schiebinger explains. The priority lies in building industries where women want to be, and a large part of that lies in gendered innovation.