We created complex systems that run critical infrastructure and we are torn between the trust in their supposedly selfless and perfectly logical processes and the fear that their obscure and unreadable algorithms will take over. This talk reflects upon the role of designers in managing these fears and expectations in the most ethical and human way.

1. The modern
Frankenstein
designing kind machines
EuroIA 2018
Clementina Gentile
@clementina_g

2. Frankenstein:
Dramatization of technophobia
Cyrus R. K. Patell

3. Rossum’s Universal Robots
Robota = forced labor

4. Is rebellion what we should
really worry about?

5. Intelligence is political

6. Alan Turing
“We are not disconnected agents, but
informational organisms sharing with
biological & engineered agents an
informational environment”
Machine as agents

7. Ethics in humanogy?

8. Data ethics framework
Data
• Privacy
• Trust
• Transparency
def find_max (L):
max = 0
for x in L:
if x > max:
max = x
return max
Algorithm
• Responsibility and
accountability
• Ethical design
• Ethical auditing
Data driven
practice
• Deontological
code
• Consent
• Secondary use
Floridi - Taddeo

9. Epistemic Normative
Brent Mittelstad
Ethical concerns in
algorithm ethics
How does it
work?
What is the
outcome?

10. Inconclusive
evidence

11. Inscrutable evidence

12. Misguided evidence

13. Unfair outcomes

14. Transformative effects

15. Transformative effects

16. Unfair outcomes Transformative effects
Harms of allocation Harms of representation
Immediate
Quantifiable
Discrete
Transactional
Long term
Difficult to formalise
Diffuse
Cultural
Kate Crawford

17. Stereotyping
Harms of representation
Yilun Wang & Michal Kosinski - Standford University

19. Recognition
Harms of representation
Joy Buolamwini

21. Denigration
Harms of representation

23. AI can get very good at
being unfair and
biased

24. How can we build a
kind Frankenstein?

25. 1
Data is not neutral

26. 1
Track lifecycle of dataset
Labeled faces in the
wild:
13 thousand images
70% male
84% white

27. 2
Categories are
never neutral

28. 2
Challenge categories
Gender? Race?

29. 3
Code is not a black box

30. 3
Strive for transparency
https://www.gov.uk/guidance/6-make-
your-work-transparent-and-be-
accountable

31. 4
Outcomes are not
objective

32. 4
Audit

33. 5
You are probably excluding
or even harming someone

34. 5
Use the panda!
Per Axbom

35. “A sustainable future hinges on
design practitioners' ability to
choose which futures are preferable.
What we introduce into the world
will always have social, cultural, and
ethical consequences”
Thomas Wendt

36. Rene Carmille

37. EuroIA 2018
Clementina Gentile
@clementina_g