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← A quest for inclusion | Myladie Stoumbou | TEDxVitosha

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Showing Revision 16 created 04/28/2020 by Tanya Cushman.

  1. Almost one year ago -
    it was February, I believe, 2019 -
  2. I was browsing in the Internet,
    and I saw this.
  3. Women built the tech industry.
  4. Then they were pushed out.
  5. Pretty powerful headline, don't you think?
  6. Actually, this is true.
  7. Women were at the forefront
    of the tech industry
  8. back when technologist jobs
    were considered menial, akin to typists.
  9. In 1946, the US military
    selected a team of people
  10. to work on the world's first computer.
  11. Guess what.
  12. More than 50 percent were women.
  13. The lady in the picture
    is Margaret Hamilton.
  14. She was the head of the coding team
    that charted Apollo 11's path to the moon.
  15. Another most renown lady
    of those times was Grace Hopper.
  16. Grace Hopper was a US Admiral
    who invented the first compiler.
  17. It was actually her programming
    and coding skills
  18. that enabled the United States
    to model the impact of the atomic bomb.
  19. Let's look at some numbers.
  20. Thirty-seven percent is the estimated,
    by the US federal government,
  21. number of women, percentage of women,
  22. that were taking computer studies
    between the '70s and the '90s.
  23. This was actually the peak.
  24. Thirty-seven percent, early '90s,
  25. was the peak in the percentage of women
    who attended computer science fields.
  26. At that time, the conditions
    of working in the tech industry
  27. were not so glamorous,
  28. but the women were known
    for their meticulous work ethic
  29. and their attention to detail.
  30. One of the great anecdotal
    stories of that time
  31. was how Grace Hopper
    identified the first-ever computer bug.
  32. She traced a glitch in the system
    to a moth trapped in a relay wire.
  33. Today in the US, there's an annual
    conference every year named after her
  34. to celebrate women in computing.
  35. What happened afterwards
  36. is that the computer industry
    became more lucrative.
  37. The tech jobs were of a bit better status
  38. and higher paid.
  39. So the tech companies
    were looking to find the best profile
  40. that would fit an engineering job.
  41. But they didn't know,
    because the coding skill was really new.
  42. So what they did is they developed
    a personality test,
  43. a personality test
    that would help them identify
  44. what a good computer engineer
    would look like.
  45. That personality test
    was actually favoring people
  46. who were not so social,
    who were not so extroverted.
  47. It was the birth of the geeky profile.
  48. (Laughter)
  49. A computer engineer
    could only be nerdy and antisocial.
  50. That was the industry demographic
    from then on, okay?
  51. So what we know today as a stereotype,
  52. of the male who's nerdy
    and anti-social as a programmer,
  53. was actually part of this vicious circle
    that started at that time.
  54. As the personality test favored more men,
  55. then the industry got more
    representation of men,
  56. then that fueled the public perception
  57. that computer engineering
    is only suitable for men.
  58. So where we are today.
  59. In 2018, it was March, I think -
    March 8th, International Women's Day -
  60. the European Commission announced a study.
  61. The study was named
    "Women in the digital age,"
  62. and, actually, it has
    a lot of concerning facts
  63. about the representation of women
    in the European tech industry.
  64. So today, there are four times more men
  65. taking up computer-related fields
    or tech-related fields.
  66. Based on the same study,
  67. out of 1,000 female graduates
    of universities in Europe,
  68. 24 are studying in tech-related fields,
  69. but then only six
    end up working in a digital job.
  70. For every 1,000 of male graduates,
  71. 49 end up working in a digital job.
  72. What's even more concerning
  73. is that women are leaving the digital jobs
    more often than men.
  74. Actually, in 2015, it was measured
  75. that nine percent of women
    left the industry.
  76. The same year, the percentage
    for men was 1.2 percent.
  77. And this happened
    at the age between 30 and 44,
  78. which is, of course,
    the primary working age.
  79. At the same time, it is the age that most
    Europeans are having their first child.
  80. So the gender gap is increasing.
  81. If we talk about digital entrepreneurship,
  82. the European Startup Monitor
  83. measures the startups
    that have female founders to 14.8 percent.
  84. Startups that have only female founders
  85. got only five percent of the global
    venture capital investment.
  86. And the average investment
    on the female-founded startups
  87. is dropping every year
    of the last five years.
  88. I graduated computer engineering
    in the early '90s.
  89. It was the time that the gender gap
    was not so visible in the numbers,
  90. but, of course, the unconscious
    and sometimes conscious bias was there.
  91. I had to sign up for volunteering work
    at the computer center in the university
  92. and, of course, miss a lot of parties
    outside of school to be taken seriously
  93. by my professors and my classmates.
  94. You see, I was not the geeky profile,
  95. and femininity at that time was actually
    adversely related to brilliance.
  96. When I got into the professional life,
    things seemed easier and better.
  97. I was learning everyday,
    I was growing every day,
  98. and I had respect from my colleagues.
  99. And then I decided that it was about time
    to move up with my career,
  100. to move on, to develop my career.
  101. "You can't - you are a mother.
  102. What would your husband say?
  103. Who would take care of your kids?
  104. How will you manage the long hours?"
  105. Surprisingly, nobody asked me before
    how I managed the long hours
  106. when I had to deliver a complex project
    with quality and on time.
  107. (Applause)
  108. My husband is in this room today,
    and I guess he's smiling.
  109. (Applause)
  110. Today, I am thankful
    for this painful moment of truth
  111. because it made me stronger,
  112. because it fueled my determination.
  113. So I worked really hard to make it happen.
  114. I worked really hard
    to make my dream come true.
  115. And, of course,
    there was advancements,
  116. and, of course, there were setbacks,
  117. and, of course, I was faced
    with unconscious bias many times,
  118. which was not only gender,
    I have to tell you.
  119. In the years of the Greek crisis,
  120. I had to to overcome the unconscious bias
    that the Greeks are incompetent, okay?
  121. Very incompetent and lazy
    and corrupt, okay?
  122. (Laughter)
  123. But I made it.
  124. And today, today I feel
    I need to share this story
  125. because I want to empower young women
    to follow up on her dreams.
  126. So this evolution that I have lived,
  127. because technology has been growing
    exponentially over the past years,
  128. was not an inclusive one.
  129. More than 50 percent of the population
    has not been part of that.
  130. But is it only gender that was left out?
  131. It was not only gender,
  132. because diversity doesn't have to do
    only with whether you are male or female.
  133. It has to do with age.
  134. It has to do with ethnicity.
  135. It has to do with color.
  136. It has to do with religion or culture.
  137. Take for example the older people today.
  138. Today, we are living in a time
  139. that we are having for the first time
  140. five generations at the same time
    in the workplace.
  141. In the next five years, analysts expect
  142. that 25 percent of the workforce
    will be over the age of 55.
  143. How do we make sure
    these people are not excluded?
  144. How do we drive
    cross-generational collaboration?
  145. How do we give opportunities
    for them to thrive
  146. and to leverage
    the benefits of digitization?
  147. Why is this discussion
    happening right now?
  148. It is happening first of all because
    there is a business case around it.
  149. Based on McKinsey,
  150. companies who have top female executives
  151. and have good diversity
    rate in their executive boards
  152. are 35 percent more likely
    to be more profitable.
  153. The European Union has estimated
    the annual productivity costs
  154. of this nine percent of women
    leaving the workplace
  155. to be 16 billion euros.
  156. But it's not only the country
    or the company financials.
  157. Diversity is an action.
  158. Inclusion is a culture.
  159. Belonging is a feeling.
  160. And the need for all three
    is deeply human.
  161. When we empower people,
  162. when we give them the opportunity
  163. to bring their strengths
    and the best talents,
  164. then we all win.
  165. And today, it's even more than that.
  166. With the advance
    of technology, such as AI,
  167. a lot of things, a lot of activities -
    like, for example, recruiting -
  168. have been outsourced.
  169. So in 2018, there was a Reuters article
  170. that revealed that one of the major
    e-commerce companies globally,
  171. a machine-learning-fueled recruiting bot,
  172. was disqualifying women over men.
  173. Τhe machine-learning algorithm
  174. used CVs that were gathered in the company
    for a period of more than 10 years
  175. to learn and identify patterns
  176. that would drive
    what would a good candidate be.
  177. Of course, those CVs
    were coming mostly from men.
  178. So the bot, when it was finding
    words like "women" in the CV -
  179. like participation
    in a women's soccer club -
  180. it would disqualify that CV.
  181. Biases that exist in the data
  182. in a world that is becoming
    more and more data-driven,
  183. in a world the decisions
    are taken based on this data
  184. can be reproduced, can be reinforced,
  185. can become a self-fulfilling prophecy.
  186. I am Greek; I was raised in Greece.
  187. I was educated with the principles coming
    from the ancient Greek philosophers.
  188. And as old-fashioned
    as this might look today,
  189. it is actually some ethical values,
    some timeless values
  190. that we need all to go back to.
  191. We need a new ethical coach
    in technology today
  192. that would earn trust,
  193. that would help technology be trustworthy,
  194. that would make us believe
    that it will be used in a fair way.
  195. So when we talk about new technologies,
    like artificial intelligence,
  196. we want them to be fair,
    to not have biases.
  197. We want them to be transparent
  198. for us to understand how data are used,
    how decisions are made.
  199. We want them to work
    in a safe and reliable way.
  200. We want them to respect our privacy.
  201. We want them to make sure
    they include and they empower everyone.
  202. So we know we need inclusive technology.
  203. To do that, I would refer
    to Simon Sinek's talk "Start with why."
  204. "Why" matters.
  205. Why are we building technology?
  206. Technology is here
    to enhance our capabilities,
  207. to help us make smarter decisions faster,
  208. to help us make less mistakes.
  209. "Why" matters.
  210. Then "who."
  211. Who's involved
    in building the technology?
  212. We need more diverse teams
    building the technology.
  213. "Who" matters.
  214. And then, finally, "how,"
  215. how transparent we are about
    the way we are building technology.
  216. How are we using the data?
  217. How are the machines learning?
  218. "How" matters.
  219. In an evolving digital world,
  220. as we welcome robots,
    holograms, artificial intelligence
  221. and more and more automation,
  222. people are getting more and more scared
    what the future will be.
  223. What will be the future of jobs?
  224. What will be the future of us as humans?
  225. The future is as good as we make it.
  226. Technology has always been here
    to enhance our capabilities.
  227. If you ask me, what would be
    our future as humans?
  228. I would tell you,
  229. we need to get back to our core identity,
  230. to the core of who we are,
  231. and that's empathy.
  232. Empathy is not a "nice to have."
  233. Empathy is absolutely essential
    to us as humans into the future.
  234. Empathy is about overcoming biases.
  235. It's about respecting each other.
  236. It's about understanding
    or trying to understand.
  237. It's about listening.
  238. It's about showing compassion.
  239. It's about offering support.
  240. The future will be good
  241. if it only includes and embraces
    every talent on earth.
  242. The future will be good
    if it only relies on empathy.
  243. In a digital world that would respect
    every individual's talents,
  244. that would empower everybody
    to get high, higher, highest,
  245. these headlines will not be there anymore.
  246. Today, I have one ask of you.
  247. Let's make this decision collectively.
  248. Inclusion starts with I.
  249. It starts with each and every one of us.
  250. Let's act, let's listen, let's understand,
    let's respect each other.
  251. Let's empathize.
  252. Let's make this evolving
    digital world a more inclusive place.
  253. Thank you.
  254. (Applause)