AI News, <?xml version="1.0" encoding="UTF-8"?>Let's Make Gender Diversity in Data Science a Priority Right from the Start

<?xml version="1.0" encoding="UTF-8"?>Let's Make Gender Diversity in Data Science a Priority Right from the Start

The emergent field of data science offers the opportunity to narrow the gender gap in STEM (in which only 13% of the engineering workforce and 25% of the computer and mathematical sciences workforce are women [2]) by making diversity a priority early on.

In addition to this being the right thing to do, it is the smart thing to do: studies show that companies with employees characterized by diverse inherent traits (traits you were born with) and acquired traits (traits you gain from experience) are 45% more likely to report a growth in market share over the previous year, and 70% more likely to report capture of a new market [3].

To evolve data science in a way that makes it a rewarding and sustainable career choice for women, we need to address two challenges: how can we increase the number of women acquiring skills and working in data science, and how can we evolve organizations and professional cultures to better retain and advance women in data science?

<?xml version="1.0" encoding="UTF-8"?>Let's Make Gender Diversity in Data Science a Priority Right from the Start

The emergent field of data science offers the opportunity to narrow the gender gap in STEM (in which only 13% of the engineering workforce and 25% of the computer and mathematical sciences workforce are women [2]) by making diversity a priority early on.

In addition to this being the right thing to do, it is the smart thing to do: studies show that companies with employees characterized by diverse inherent traits (traits you were born with) and acquired traits (traits you gain from experience) are 45% more likely to report a growth in market share over the previous year, and 70% more likely to report capture of a new market [3].

To evolve data science in a way that makes it a rewarding and sustainable career choice for women, we need to address two challenges: how can we increase the number of women acquiring skills and working in data science, and how can we evolve organizations and professional cultures to better retain and advance women in data science?

Science doesn’t explain tech’s diversity problem — history does

In 2017, the idea that biological differences drive social inequality is considered fairly offensive.

But unlike people, not all ideas are created equally, and they should not be treated with the same amount of seriousness — especially when those ideas ignore both a broad scientific debate that’s gone on for years and clear evidence that women in tech are excluded more than in other industries.

Programs aimed at boosting diversity in tech are “as misguided and biased as mandating increases for women’s representation in the homeless, work-related and violent deaths, prisons, and school dropouts,' he writes.

Damore pushed back on Friday in a Wall Street Journal op-ed, writing that the document was “a reasoned, well-researched, good-faith argument.” He asks: “How did Google, the company that hires the smartest people in the world, become so ideologically driven and intolerant of scientific debate and reasoned argument?” The answer is that the debate is old and the arguments are well-worn and generally bad.

The memo’s deficiencies make more sense once you understand that it’s not a real attempt to grapple with facts or reality, but is really just an expression of Damore’s feelings — a reactionary flailing to justify a broken status quo.

When counting non-technical workers, companies are much closer to gender parity: Google employs 48 percent women, Microsoft with 39 percent, Apple has 38 percent, and Facebook 60 percent.

Not only are more women going to college and majoring in STEM fields than ever before, girls are keeping pace with boys in high school, earning the same number of credits in math and science and even earning slightly higher grades in those subjects.

“Reducing female attrition by one-quarter would add 220,000 people to the highly qualified [science, engineering, and technology] labor pool,” says a 2008 Harvard Business Review research report.

other studies have simply called it “unfairness.” At its most benign, “good old boy culture” will merely slow a woman’s career trajectory, but at its worst, women experience intimidation, hostility, and harassment.

Some unfortunate facts about Western history that James Damore neglects to mention (e.g., women not being considered people) have made it so that women have had no choice but to spend the last century or so carving out spaces for themselves in all kinds of male-dominated professions.

This is a culture that Kara Swisher describes as “ruled by the equivalent of badly-raised boys who eschewed the kind of discipline and rigor that is the real requirement of success.” That’s the best way to make sense of the numbers, the trends, and the attrition — and it’s the hypothesis that fits with the anecdotes that engineers like Susan Fowler have published about toxic cultures at companies like Uber.

“It’s almost strange to have to rationally refute it, because it is just so wrong,” says tech historian Marie Hicks, a professor at the University of Wisconsin-Madison and author of the book Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing Women powered the tech sector during and after World War II, using the codebreaking computer called the Colossus in the UK.

Hicks reports that in 1955, the UK’s Aeronautical Research Department said, “Boys generally prefer laboratory work to computing… this might be due in part to the absence of any recognized career in computing and of any suitable specialist courses.” Then the power of computing started to become clear — and suddenly this low-paid women’s grunt work started to look appealing to men.

“It’s crazy-making to say that women aren’t good at technical pursuits, or that there’s any reason other than structural inequality that’s making women stay away from computing work.” In the 1960s, big employers like the System Development Corporation (SDC) and IBM began to rely heavily on personality profiles and aptitude tests to hire programmers, writes Nathan Ensmenger, a professor at Indiana University.

The personality profiles of successful programmers, created by psychologists starting in the late 1960s, codified many of the stereotypes that abound today, like how “programmers dislike activities involving close personal interaction.

In 1968, a respected industry analyst described the programmer as “often egocentric, slightly neurotic, and he borders upon a limited schizophrenia.” (Oddly, in 1968, being neurotic made men better programmers.) “The incidence of beards, sandals, and other symptoms of rugged individualism or nonconformity are notably greater among this demographic group.

The cultural idea of a programmer went from the ENIAC girl to a beardy weirdo in sandals, and from there created a self-fulfilling prophecy and feedback loop between culture and industry that has persisted into the present day.

When symphony orchestras started conducting blind auditions, for example, women began being hired more frequently — not out of any sort of favoritism, but because the screens prevented bias from tainting the hiring process.

“He frames this whole thing as if it’s about intellectual biases but he misses completely, omits, ignores, analyses of actual power and discrimination — who has been closed out systematically?” says Rebecca Jordan-Young, professor of Women’s, Gender, and Sexuality studies at Barnard College.

Damore’s own attempt at this classic maneuver is shot through with a number of fallacies, the biggest of which is the structural fallacy of ignoring history while assuming that the burden of proof rests with diversity advocates to show that inequality is the result of discrimination rather than the inevitable consequence of biology.

Solitary work is something that only happens at the most junior levels, and even then it’s only possible because someone senior to you — most likely your manager — has been putting in long hours to build up the social structures in your group that let you focus on code.” Let’s set that aside for just a moment, however, and assume that doing “tech” means you don’t have to deal with people or feelings.

He goes on to say, “We haven’t been able to measure any effect of our Unconscious Bias training and it has the potential for overcorrecting or backlash, especially if made mandatory.” Without providing any details or support or citations — only speculation and what may even be his own emotional reaction to having undergone Unconscious Bias training — Damore argues that Google’s diversity initiatives have not been adequately considered or rigorously vetted.

A spokesperson for Google pointed to things like a six-week increase in maternity leave (from 12 weeks to 18), active recruiting of women candidates, internships for women, and a summer camp for graduating high school seniors.

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