This document discusses inclusive technology and how to make technology benefit everyone. It begins by asking if inclusive tech is possible and defines inclusive tech as technology that benefits all people. It then provides examples of where technology has not been inclusive, such as voice recognition software being more likely to recognize male voices. The document discusses how biological factors and binary thinking can lead to bias in technology. It advocates considering diversity in data collection and design, being transparent about algorithms, and involving diverse groups in the design process. The overall message is that while technology can help humanity, we must ensure it is developed and applied in a sustainable and inclusive way.

1. Inclusive tech, (how) is that
possible?
Marion Mulder

2. Inclusive tech, (how) is that possible?
• Is inclusive tech possible? Spoiler alert: YES (but its hard work)
• What do I mean when I talk about inclusive tech? I’ll take you
along on my journey of exploration
• That ‘thing’ about biological and genetics aspects; what’s going
on? why is that? Symptoms & underlying causes. In short: Bias
and by Binary Thinking
• Opportunities & Possible Solutions

3. Future Maker
Creating a world I want to live in

4. Equality for all
Taking away hurdles

5. How do I try to do that?

6. Activism

7. Talks
conversation

8. Digital
Transformation
work on digital strategies and
implementations of solutions

9. Inclusive Tech
Technology that benefits everyone
Privilege:

10. So what could possibly go wrong, right?

11. Some examples of where it did not go
quite right….
Sales of helmet in
Asia (of US brand)
were low…
Turns out the
average head size
in the database was
not diverse enough
Image source: Che-Wei Wang – mindful algorithms: the new role of the designer in generative design - TNW conference 2018

12. Some examples of where it did not go
quite right….
Automatic translations also changes gender

13. Some examples of where it did not go
quite right….
Google’s image classifier
wrongly assigned black
people

14. Some examples of where it did not go
quite right….

16. https://www.nytimes.com/interactive/2020/11/21/science/artificial-intelligence-fake-people-faces.html
Because we can, should we?

20. Source: Virginia Dignum – UMEA University
Because we can, should we?
How correct is the data really?

21. Because we can, should we?
Criminal face detection AI
Source: Juriaan van Diggelen - TNO

22. Contributing to the effect on society
Documentary on Netflix on modern day
slavery in the US judicial system.
As a consequence of the Law & Order
Business Model

24. Addictive technology

25. Addictive technology

26. On October 25, 2017, Saudi Arabia
granted citizenship to Sophia Robot.
In August 2019, in a positive and long-overdue development, the
authorities announced major reforms to the discriminatory male
guardianship system. Among other things, these allowed women
aged over 21 to apply for and obtain a passport and travel without
the permission of a male guardian; women aged over 18 to
register the birth of a newborn child, the death of a relative and
their own marriage or divorce, as well as to apply for and obtain a
family record; and women to act as the head of a household.
12/11/2019 “Saudi Arabia just declared
homosexuality, feminism and atheism
as ‘extremism’
It’s a girl….

27. I’m sorry, I don’t understand…..
Google’s speech-recognition software was
70% more likely to
recognise male speech than female speech
2016 Rachel Tatman,
linguistics at the University of Washington

28. So what’s causing it,
and what can we do?
In short:
Bias based on (binary view on) biological aspects

29. Why Biology Matters
There are reasons why biology
matters in technology (design),
think for example of a smart
phone generally not fitting
women’s hands, or medicine
behaving differently for men an
women due to hormones.
But in many cases we should ask
ourselves “should we? really?”
And at the same time we should
ask ourselves is this really a
gender thing or is this just a
‘people with smaller hands thing’
or a ‘people with uteruses/
specific hormone behaviour
thing’?

30. Why Biology Matters
There are reasons why biology
matters in technology (design),
think for example of a smart
phone generally not fitting
women’s hands, or medicine
behaving differently for men an
women due to hormones.
But in many cases we should ask
ourselves “should we? really?”
And at the same time we should
ask ourselves is this really a
gender thing or is this just a
‘people with smaller hands thing’
or a ‘people with uteruses/
specific hormone behaviour
thing’?
PS: gender =/= women!
It is the entire spectrum of
men – women.
Meet ‘default man’
skinny white dude 1,75m hight

31. Why Biology Matters
There are reasons why biology
matters in technology (design),
think for example of a smart
phone generally not fitting
women’s hands, or medicine
behaving differently for men an
women due to hormones.
But in many cases we should ask
ourselves “should we? really?”
And at the same time we should
ask ourselves is this really a
gender thing or is this just a
‘people with smaller hands thing’
or a ‘people with uteruses/
specific hormone behaviour
thing’?
PS: gender =/= women!
It is the entire spectrum of
men – women.
Meet ‘default man’
skinny white dude 1,75m hight

32. Dealing with human Bias
• Bias: expectations derived from experience
regularities in the world. Knowing what
programmer means, including that most are male.
• Stereotypes: biases based on regularities we do
not wish to persist.
Knowing that (most) programmes are male.
• Prejudice: action on stereotypes.
Hiring only male programmers.
Bias, stereotypes, prejudice;
discrimination potentially being programmed into the future
Source: Joanna Bryson at AI Summit - Caliskan, Bryson &Narayanan 2017
Ada Lovelace

33. Live isn’t binary
M U L D I M E D I A
• The problem with binary is that there’s
usually a hierarchy in ‘m; one is better
that the other:
man/woman, white/black, abled/disabled,
in a relationship/single, straight/gay, left
brain/ right brain, economy/environment,
us/them, etc.
• What if we started looking at the world
from a non-binary perspective and more
as a spectrums?

34. How we view the world isn’t static
yesterday ≠ today ≠ tomorrow

35. So if we DO collect data or use historical
data sets……

36. Just because
we can,
should we?
What is the by-catch/ what are side
effects?

37. Just because
we can,
should we?
What is the by-catch/ what are side
effects?
(How) is this
helping
Human Kind
in a sustainable
way?

38. Design & TEST Human in the Loop
systems
• Consult non-technical experts and resources
• Involve diverse groups in your design, data
gathering, testing
• Transparency (explainable) of your decision
making and algorithms
• Analyze –synthetize –evaluate -repeat

39. https://drive.google.com/file/d/1T8jGIG2bvbQtE7fLWLSRrmC9yYwMeBuf/view

40. https://theodi.org/article/data-ethics-canvas/

41. Access & Accessibility
Is what you are making available to everyone?
Should this ‘problem’ actually be solved by (this) technology?
AmazonBasics
voice-controlled
microwave oven

42. Access & Accessibility
Is what you are making available to everyone?
Should this ‘problem’ actually be solved by (this) technology?

43. Access & Accessibility

44. Be the face of the
change you want to see
join a community

45. Lets create the
future together!