Bath Ruby 4 - Janet Crawford - The Surprising Neuroscience of Gender Inequality

  • Not a coder, but a scientist
  • Will try to explain why the gender inequality in our industry exists
  • Messages growing up:
    • on the one hand message from rocket scientist father that being a scientist was a good goal for a woman
    • On the other hand, birthday gifts between her (clothing, crafting) and her brother (tinkering, electrical engineering) were very gendered
  • why does this happen? Why do parents make these choices (despite their actual values)
  • At university, message was: ‘because you don’t know soldering, you don’t belong on a physics degree
  • unconscious bias

@JanetCrawford talking about unconscious

  • Women in CS peaked in around 1970 (30-ish%) and has been declining ever since
  • Similar stories in politics, film etc.
  • “for a lot of people this doesn’t seem to be a problem”
    • Rationale: we have parity of education and opportunity, so it must just be because women aren’t interested in these roles
  • Research: companies with more women, particularly on boards, massively outperform other companies “on every metric that business cares about”
  • There are undeniable good reasons, so why is there still such inequality
  • All of us, male and female are unconsciously gender-biased.
    • Until we learn how to bring these to the conscious level it’s going to continue to be a problem
  • Brains encode associations based on what they perceive - irrespective of accuracy etc.
    • We make decisions about people immediately based on appearance
    • Most of the time this is OK: these biases are shortcuts.
    • Things go wrong when these biases stop working (stop being accurate)
  • Brain will always prefer to take the shortcut to free up brainpower for other tasks
  • Unconscious associations can be tested by matching up traditionally gendered qualities with images of the opposite gender and measuring the difficulty of each pairing
  • Mothers overestimate the crawling ability of boys and underestimate that of girls
  • Women’s code on github PRs was accepted about 4% more than men’s (unless the gender could be inferred from their profile, which reversed the trend)
  • Women are more likely to attribute their success to luck, men to hard work
  • Confidence and likeability are positively correlated for men, and negatively correlated for women
  • 2 environments for a Computer Science classroom: one neutrally decorated, one geekily decorated. No difference in impact on Men’s interest in the subject, but the latter significantly reduced the interest in the course of the female participants
  • How to fix it? Focus on creating a neutral environment - consider all aspects: decor, imagery etc.

“Legislation doesn’t change culture: people do”

“It’s hard work and it’s messy, but it’s a mess worth wading into