Gender Equality in Tech: There’s more to the story than population differences between the genders

Mitsu Hadeishi
Aug 13, 2017 · 3 min read

In response Damore’s manifesto, “Heterodox Academy”, a group that includes multiple political points of view, posted that the scientific evidence is strong that there are population-level differences between the genders, though there’s little to no evidence of a difference in ability, only in interest/preference. However, the assertion that there are population-level differences between the genders is not the central issue.

  1. Damore refers to both interest and ability in his manifesto as reasons why women are not as prevalent as men in software engineering. Yet, as their survey concludes, the evidence currently shows there isn’t a difference between men and women in ability — this has certainly been my experience as a software engineering manager.
  2. There is, however, evidence for differences in interest level — but, again, contrary to what one would predict if this difference were primarily due to innate biological factors that could not be changed — the evidence shows level of interest in software engineering varies quite a bit across cultures, and it also has varied considerably within our own culture — in 1987, for instance, 37 percent of CS majors were female, and today, it is below 20 percent. Yet other STEM fields have shown a continual march towards parity between the sexes. This suggests that interest level may be driven by many factors changeable by cultural and environmental factors, and is not, as Damore claims, largely explicable due to unchanging “biological” factors.
  3. The biggest problem with the essay is only evident when you read it as a whole. He begins by saying he believes that sexism is part of the reason for different outcomes — — yet then goes on to call efforts to directly combat sexism-based causes of differential outcomes “discriminatory”, and claims they cause “irreparable harm”, then lists out only remedies which are ones based on the assumption that differences in outcomes are due to biology — — it’s disingenuous at best, because although he claims to believe that sexism is real, he doesn’t believe any attempts to combat sexism directly are valid. There is considerable evidence that unconscious bias remains a significant problem impeding women and minorities in their careers, yet Damore presents only strategies which presume the reason for differences in outcomes is innate “biological” difference.
  4. Furthermore, Damore ALSO lists racial diversity programs as problematic and harmful, yet doesn’t discuss any scientific data for this. Is he also implying the reason for the underrepresentation of, say, African-Americans, is due to innate biological differences between African-Americans and whites?
  5. The fact that he only cites one type of evidence, and only discusses in negative terms any mechanisms to combat bias-based reasons for differential outcomes, shows motivated reasoning and confirmation bias, despite his criticisms of the same. He’s reinforcing the notion that existing differential outcomes are “natural” and based on biological, unchanging differences, even though the actual scientific evidence is far more complex.
  6. As Jonatan Zunger wrote, however, in the end the biggest problem with the manifesto is that, combined with the above flaws in reasoning and use of evidence, he posted it in a work context, in an attempt to persuade his fellow employees that his view that women are biologically different from men in ability (not only in preference) is one of the main reasons for the different prevalence of women in the software engineering workforce. This has the obvious effect of putting a great deal of pressure on existing female engineers, forcing them to feel they have to struggle to work even harder to justify their suitability for their jobs, thereby causing Google to become a far more difficult place for them to work and impacting the company’s operational effectiveness significantly. Ironically, he is perhaps bolstering his hypothesis that men tend to have a poor understanding of people by his own anecdotal example. He certainly isn’t adding to the case, however, that lacking an understanding of people is an advantage in a software engineering career.

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