The gender gap in computer science research won’t reach equality for more than a century, according to new research released Friday that showed computer science isn’t just lagging behind, it’s also going in the wrong direction.
An analysis of 2.87 million computer science research papers between 1970 and 2018 shows that “under our most optimistic projection models, gender parity is forecast to be reached by 2100, and significantly later under more realistic assumptions,” researchers wrote. “In contrast, parity is projected to be reached within two to three decades in the biomedical literature. Finally, our analysis of collaboration trends in Computer Science reveals decreasing rates of collaboration between authors of different genders.”
The researchers laid out exactly how long of a timeline we’re looking at:
The study, first reported by the New York Times, crystallizes a fundamental gender problem in tech. Other fields, like biomedicine, are in considerably better shape.
The tech industry has a diversity problem that manifests in too many ways to count. For one, groups of disproportionately white and male computer scientists are worse at understanding what how science, products, apps, and services might impact the lives of people who aren’t exactly like them: Women, in this case, and people of color as well. That’s to say nothing of what it means to be a woman in these sometimes hostile workplaces.
The problem has bubbled to the surface at giant tech companies like Facebook, Microsoft, and Google which are collectively building a tech future that will define the next century. No big deal.
Maybe the most obvious example is the rise of face recognition surveillance, technology powered by artificial intelligence and machine learning. In use around the world, here’s what the technology means in the real world:
Earlier this year, MIT researchers Joy Buolamwini and Timnit Gebru highlighted one of the ways face recognition is biased against black people: darker skinned faces are underrepresented in the datasets used to train them, leaving facial recognition more inaccurate when looking at dark faces. The researchers found that when various face recognition algorithms were tasked with identifying gender, they miscategorized dark-skinned women as men up to a 34.7 percent of the time. The maximum error rate for light-skinned males, on the other hand, was less than 1 percent.