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SF Ruby/Women Who Code/PyLadies - Programming Diversity

SF Ruby/Women Who Code/PyLadies - Programming Diversity

Presented at Causes in San Francisco, CA

29ff20a2ac375ff989f3bff70b71b23d?s=128

Ashe Dryden

June 06, 2013
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Transcript

  1. @ashedryden Programming Diversity

  2. @ashedryden Ashe Dryden @ashedryden ashedryden.com

  3. @ashedryden What is Diversity?

  4. @ashedryden more than gender

  5. @ashedryden various backgrounds, experiences, and lifestyles

  6. @ashedryden not always visible

  7. @ashedryden race gender sexuality ability language appearance physical & mental

    health age socioeconomic class and more!
  8. @ashedryden Intersectionality the interactions of biological, social, and cultural traits

    contributing to systemic inequality
  9. @ashedryden race gender ability physical & mental health socioeconomic class

  10. @ashedryden in the US, women on average earn 80.9% of

    what men do Source: ABC: How to end the wage gap between men and women, http:/ /abcnews.go.com/ABC_Univision/News/women- make-men/story?id=18702478#.UZt3yitASqk
  11. @ashedryden but Latina women earn 59.3% of what white men

    do Source: ABC: How to end the wage gap between men and women, http:/ /abcnews.go.com/ABC_Univision/News/women- make-men/story?id=18702478#.UZt3yitASqk
  12. @ashedryden the unemployment rate in the US is ~7.5% Source:

    High Rate of Unemployment for the Blind, http:/ / work.chron.com/high-rate-unemployment-blind-14312.html
  13. @ashedryden the unemployment rate for the blind is 70-75% Source:

    High Rate of Unemployment for the Blind, http:/ / work.chron.com/high-rate-unemployment-blind-14312.html
  14. @ashedryden Privilege unearned advantages a person gets for a perceived

    trait they possess, putting them in the “normal” group
  15. @ashedryden Better Education Access to Technology at an Earlier Age

    Higher Pay Assumed Competency Quality of Social/Professional Network Seen as Skill Set Instead of Traits Easily Fit Into/Identify with Subculture
  16. @ashedryden Stereotype Threat anxiety or concern where a person has

    the potential to confirm a negative stereotype about their social group
  17. @ashedryden Source: xkcd, How it Works: http:/ /xkcd.com/385/

  18. @ashedryden Impostor Syndrome a psychological phenomenon in which people are

    unable to internalize their accomplishments
  19. @ashedryden this is especially pronounced when negative stereotypes exist about

    a group a person is in
  20. @ashedryden less likely to apply for certain jobs

  21. @ashedryden less likely to submit a talk to a conference

  22. @ashedryden less likely to attend to a conference

  23. @ashedryden Marginalized Someone pushed to the edge of a group

    and accorded lesser importance; a minority or sub- group being excluded, their needs or desires being ignored.
  24. @ashedryden society teaches us to do this to everyone within

    marginalized groups
  25. @ashedryden “I’m different. I’m logical and rational; I don’t see

    gender or race.”
  26. @ashedryden Source: Moss-Racusin, et al. Science faculty’s subtle gender biases

    favor male students, 2012 scientists & STEM professors do this to other scientists & STEM professors
  27. @ashedryden even marginalized people do it to people within their

    same social groups
  28. @ashedryden How diverse is the tech industry?

  29. @ashedryden Women make up 24% of the industry Source: FLOSSPOLS

    - Gender Integrated Report Findings
  30. @ashedryden ...but only 1.5-3% of OSS contributors Source: FLOSSPOLS -

    Gender Integrated Report Findings
  31. @ashedryden Source: Mercury News. Blacks, Latinos, and Women lose ground

    in tech companies, 2011 Women Hispanic Black Asian White 0% 25% 50% 75% 100% Tech Industry US Population Tech Industry vs US Population
  32. @ashedryden Lack of Diversity is a Global Problem

  33. @ashedryden India 8% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  34. @ashedryden US 17% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  35. @ashedryden UK 18.2% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  36. @ashedryden France 20% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  37. @ashedryden Brazil 20% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  38. @ashedryden South Africa 25% of CS students Source: Anita Borg

    Institute, State of Women in Technology Fields Around the World
  39. @ashedryden “Maybe women just aren’t interested in programming.”

  40. @ashedryden “Or maybe women aren’t biologically predisposed to programming.”

  41. @ashedryden there exists no physical or biological difference that impacts

    a person’s ability to be a programmer
  42. @ashedryden the differences that exists are purely social and cultural

    constructs, and are therefore able to be overcome
  43. @ashedryden Bulgaria 73% of CS students Source: Anita Borg Institute,

    State of Women in Technology Fields Around the World
  44. @ashedryden Diversity Matters

  45. @ashedryden Diversity Matters to businesses

  46. @ashedryden sales revenue, number of customers, market share, and profits

    relative to competitors increase Source: Does Diversity Pay?, Cedric Herring, AMERICAN SOCIOLOGICAL REVIEW, 2009, VOL. 74 (April:208–224)
  47. @ashedryden solve complex problems better and faster Source: Scott Page,

    The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press, 2009
  48. @ashedryden more creative & stimulated through persistent exposure to minority

    perspectives Source: Charlan Jeanne Nemeth, Differential Contributions of Majority and Minority Influence.
  49. @ashedryden make better decisions, generate more innovation Source: Caroline Simard,

    Ph.D., Obstacles and Solutions for Underrepresented Minorities in Technology, at 8, Anita Borg Institute for Women and Technology (2009)
  50. @ashedryden the financial success and viability of a company are

    directly related to the makeup of its teams
  51. @ashedryden Diversity Matters to society

  52. @ashedryden heal the issue of unequal pay and opportunity

  53. @ashedryden create class mobility

  54. @ashedryden the wage gap is smaller in STEM fields

  55. @ashedryden in tech women earn about 87% of what men

    do
  56. @ashedryden Why the lack of diversity?

  57. @ashedryden Pipeline

  58. @ashedryden Cultural Cues

  59. @ashedryden difference in toys and games for boys and girls

  60. @ashedryden no famous role models that represent them

  61. @ashedryden Access to Technology

  62. @ashedryden on average, men get access to their first computer

    at 11
  63. @ashedryden women get access to their first computer at 14

  64. @ashedryden African American and Hispanic households have lower computer ownership

    rates and broadband adoption
  65. @ashedryden African American and Hispanic are adopting smart phones at

    a much higher rate than their white counterparts
  66. @ashedryden Access to Quality Education

  67. @ashedryden quality high school education is one of the greatest

    indicators of earning potential
  68. @ashedryden schools in poor neighborhoods have lower quality math and

    science programs
  69. @ashedryden Access to healthcare

  70. @ashedryden women are more likely to be caregivers

  71. @ashedryden people of color, people with disabilities, and LGBTQ people

    have less access to quality healthcare
  72. @ashedryden Attraction

  73. @ashedryden Lack of Role Models

  74. @ashedryden less likely to see people like them represented in

    companies and conferences
  75. @ashedryden Geek Stereotype

  76. @ashedryden people who don’t identify and aren’t represented by the

    geek stereotype are turned off by impression of someone who represents the stereotype Source: Enduring Influence of Stereotypical Computer Science Role Models on Women’s Academic Aspirations, Cheryan 2012
  77. @ashedryden Attrition

  78. @ashedryden 56% of women leave tech within 10 years Source:

    NCWIT, 2012
  79. @ashedryden that’s twice the attrition rate of men

  80. @ashedryden harassment

  81. @ashedryden people in a marginalized group are twice as likely

    to report being harassed or mistreated
  82. @ashedryden “I’ve never seen someone get harassed.”

  83. @ashedryden discrimination

  84. @ashedryden pay, advancement, job offers

  85. @ashedryden men are 2.7 times more likely than women to

    be promoted to a high-ranking job, such as vice president or senior manager Source: Mercury News 2010, http:/ /www.mercurynews.com/ ci_14382477
  86. @ashedryden Myriad Solutions

  87. @ashedryden Change Starts with Us

  88. @ashedryden Education is the Trojan Horse to Empathy

  89. @ashedryden Get to know people different than us

  90. @ashedryden Understand that bias and discrimination are often subtle

  91. @ashedryden Learn to Apologize

  92. @ashedryden Advocate for Change

  93. @ashedryden Talk about these issues openly

  94. @ashedryden “That’s not cool :(”

  95. @ashedryden Influence change in our communities and workplaces

  96. @ashedryden Have the hard conversations

  97. @ashedryden Increase Education and Access

  98. @ashedryden help facilitate events for marginalized people in tech

  99. @ashedryden volunteer at local schools and groups

  100. @ashedryden commit financial resources

  101. @ashedryden work with colleges and universities

  102. @ashedryden “What are you doing to help students who’ve had

    less exposure to technology?”
  103. @ashedryden remove bias from our schools and universities

  104. @ashedryden “Have you programmed before?”

  105. @ashedryden Change Our Workplaces

  106. @ashedryden what does the ‘about’ page of your website look

    like?
  107. @ashedryden vocal support

  108. @ashedryden job listing language and requirements

  109. @ashedryden benefits

  110. @ashedryden interviewing

  111. @ashedryden equal pay

  112. @ashedryden culture

  113. @ashedryden mentoring and career goal attainment

  114. @ashedryden Improve Inclusion at Conferences

  115. @ashedryden diversity in the organizing team

  116. @ashedryden work with the community

  117. @ashedryden codes of conduct

  118. @ashedryden scholarships

  119. @ashedryden anonymized proposal processing

  120. @ashedryden outreach

  121. @ashedryden there is no silver bullet

  122. @ashedryden multi-faceted problems require multi-faceted solutions

  123. @ashedryden requires participation from everyone

  124. @ashedryden Questions?

  125. @ashedryden Thank You! @ashedryden