Women in Science. Recommendations to improve Horizon Europe through a gender perspective. The European Parliament hosted a day of reflection on the role of women in science on 25 September 2018.

1. Women in STEM careers,from school to the lab
Lorena Fernández Álvarez
University of Deusto,Spain

2. The “leaky pipeline” phenomenon

3. Self-confidence

4. She’s bossy
vs
He has leadership skills

5. She’s beautiful vs He’s smart

6. She’s hard-working vs He’s brilliant

7. By the age of 6,girls are less
likely than boys to label people
of their own gender as “really,
really smart”.
Paper reference: Gender stereotypes about
intellectual ability emerge early and influence
children’s interests (Science Journal)

8. Self-confidence
Curiosity

9. Self-confidence
Curiosity
Stereotypes of STEAM fields

10. Self-confidence
Curiosity
Stereotypes of STEAM fields
Lack of female
role models

11. Self-confidence
Curiosity
Stereotypes of STEAM fields
Lack of female
role models
TV series, films...

12. Before they reach university,
we've lost them...

13. ...and after university,the pipeline is still
leaking: work–family life balance,men’s club,
mansplaining,herpeating,to continually prove
our value to the organization...

14. Why do we need to fix the leaky pipeline?

15. "When you write a
line of code,you can
affect a lot of people."
Sheryl Sandberg

16. Algorithmic bias
The blind application of machine learning runs
the risk of amplifying biases present in data

17. Google Translate
Paper reference: “Machine Translation: Analyzing Gender”

18. – Persona molona
«Voy a citar a alguien»

19. Paper reference: “Man Is to
Computer Programmer as
Woman is to Homemaker?
Debiasing Word Embeddings”
Word2vec,a popular framework to
represent text data as vectors:
• man:king :: woman:queen
• sister:woman :: brother:man
• man:boss :: woman:receptionist
• man:programmer :: woman:homemaker

20. Paper reference: “Men Also Like
Shopping: Reducing Gender Bias
Amplification using Corpus-level
Constraints”
• The visual semantic labeling (vSRL)
tasks require large quantities of
labeled data,predominantly retrieved
from the web.
• 67% of cooking images in datasets have
a woman in the agent role.
• Machine-learning software trained on
the datasets didn’t just mirror those
biases,it amplified them: they do not
recognize images of men cooking.

21. Work promotion often
starts in casual contexts

22. Paper reference: “Automated
Experiments on Ad Privacy
Settings. A Tale of Opacity,
Choice,and Discrimination”
After creating 1,000 fictitious
users (50% women,50% men):
men saw 1,800 times a Google's
ad of a high-paying job. The
women saw it 300 times.

23. Facebook is letting Job advertisers target only men
A review by ProPublica found that 15 employers in the past
year,including Uber,have advertised jobs on Facebook
exclusively to one sex.

24. Why let
technology be
created by
limited points
of view?
It's time to
prioritize
diversity
across tech.

25. Patching the “leaky pipeline”

26. Let's recover and tell the story of incredible women who
have been pioneers in science and technology and who
have suffered the invisibility “superpower”.

27. An analysis of gender in high school textbooks shows that
the presence of women in educational materials is 7.5%

28. The ‘Scully Effect’ is
real: female X-Files
fans are more likely to
go into STEAM

29. Ada Byron Prize to Women Technologist
www.premioadabyron.deusto.es

30. The power of close
role models

31. Research from Microsoft has revealed that the number of girls
interested in STEM across Europe,on average,almost doubles
when they have a role model to inspire them.

32. “I have learned that girls
can do the same as boys.”
http://inspirasteam.net

33. "Never limit yourself
because of others' limited
imagination."

34. We have to work for girls and young women so that
they can decide freely what they want to be when they
grow up. Because while professional women suffer
from glass ceilings,girls and young women suffer from
“glass corridors” that lead them under the premise of
"false freedom".

35. Europe has to promote social justice in its policies,and it is not
possible to make scientific policy by excluding half of the population.

36. @loretahur
lorena.fernandez@deusto.es
THANK YOU!