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This talk is not about programming

This talk is not about programming

A talk about gender stereotypes and unconscious bias

Lieke22

May 04, 2016
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  1. Lieke Boon @Lieke2208 www.codepancake.com This talk is not about programming..

  2. RIDDLE A father an his son are in a car

    accident. The father dies at the scene and the son, badly injured, is rushed to the hospital. In the operating room, the surgeon looks at the boy and says, “I can’t operate on this boy. He is my son”.
  3. ABOUT ME: MY JOURNEY INTO TECH • Started programming in

    2012 • Involved in Women in Tech communities: PyLadies, RailsGirls etc. • Dutch Ambassador for European Codeweek • CodePancake • Likes to talk about unconscious bias • Work in Tech: Kabisa, VHTO and currently GitHub
  4. MY STORY

  5. MY STORY

  6. WHY MORE WOMEN IN TECH? Lack of women in tech

    means: • Loss of talent for the IT industry • Loss of opportunity for females entering the job market
  7. % FEMALE STUDENTS IN STEM (HIGHER) EDUCATION (SCIENCE, MATH, COMPUTING)

    IN 2012 Source: VHTO Factsheet
  8. MOST GIRLS DROP OUT OFF IT STUDIES AFTER SECONDARY EDUCATION

    • Self-image: Girls think they perform worse than they actually do in STEM related subjects • Unfamiliarity: lack of understanding about what IT means, partly due to lack of role models • Environment: girls less stimulated by teachers and parents. There are persistent stereotyped views that the sector is better suited to men.
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  10. UNCONSCIOUS BIAS We’re constantly overlooking much of the world around

    us and there’s actually nothing mysterious about it • We receive 11 million of bits of information every day • We can only consciously process 40 bits
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  14. INCLUSION PARADOX • We are all human beings and we’re

    all alike, we share an human experience • However, we’re uniquely different • We’re surrounding ourselves with people who are ‘like us’ • Despite of the fact that we’re all well-intentioned, we’re not very inclusive of others, especially when they’re not like us source: Helen Turnbull
  15. PILOTS GENDER BIAS

  16. THE CASE WITH THE RESUMES • Student applying for Manager

    Science Lab • 50% of the scientists received applications with a male name attached. The other 50% the same application with a female name attached. • Guess what happened? Research
  17. RESULTS • “Female” applicants were rated significantly lower than the

    “males” in competence • “Female” applicants were offered a lower salary • Both male and female scientists were equally guilty of committing the gender bias
  18. MORE GENDER BIAS.. • Since the 1970’s the number of

    women in orchestra’s went up from 5% to 25%, due to auditioning behind a curtain https://www.princeton.edu/pr/pwb/01/0212/7b.shtml
  19. GENDER BIAS • Most people would agree that gender bias

    exists..in others • All of us, myself included, are biased Based on a quote from Dana Chardon
  20. TEST (LEAVING THIS EXERCISE OUT FOR THE ONLINE VERSION, BETTER

    TO TAKE IT YOURSELF :))
  21. IMPLICIT ASSOCIATIONS TEST • Explicit: how much science is associated

    with men or women • Implicit: how quickly words like ‘math’ and ‘physics’ are associated with ‘boy’ or ‘men’
  22. MY RESULTS: Your data suggest a strong automatic association of

    Male with Science and Female with Liberal Arts
  23. RIDDLE A father an his son are in a car

    accident. The father dies at the scene and the son, badly injured, is rushed to the hospital. In the operating room, the surgeon looks at the boy and says, “I can’t operate on this boy. He is my son”.
  24. None
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  26. Source: VHTO

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  28. GENDER BIAS COULD BE A MAJOR THREAT

  29. BE AWARE THAT YOU’RE BIASED Take the Implicit Associations Test

  30. USE INCLUSIVE LANGUAGE

  31. HOLD YOURSELF AND OTHERS ACCOUNTABLE.. POINT IT OUT

  32. BY THE SIMPLE ACT OF TALKING OPENLY ABOUT BEHAVIORAL PATTERNS..

    • It makes the subconscious conscious • Talking can transform minds, which can transform behaviors, which can transform communities, which can result in a better environment for (e.g.) women in tech
  33. USE YOUR IMAGINATION: COUNTER-PROGRAM YOUR BRAIN

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  35. SUMMARY 1. Awareness: accept that you’re biased 2. Use inclusive

    language 3. Hold yourself and others accountable 4. Use your imagination: counter-program your brain
  36. IT’S NOT ABOUT PROGRAMMING….. It’s about the way YOU are

    programmed
  37. Lieke Boon @lieke2208 www.codepancake.com THANK YOU!

  38. https://implicit.harvard.edu/implicit/