Questo talk è un invito a designer e innovators di tutto il mondo a partecipare, sfruttando le opportunità e affrontando le sfide dell’intelligenza artificiale per creare human(ity)-centered applications e significative user experiences. Partiremo con un corso intensivo sull’intelligenza artificiale e il machine learning, poi ci interrogheremo sul ruolo dei designer, esplorando alcuni aspetti critici della progettazione, su come applicare le nostre competenze di designer per avvicinare l’IA a valori sociali, economici e per l’utente. Infine presenteremo una panoramica pratica di come utilizzare il design thinking process che conosciamo e condurlo a quello meno familiare dell’intelligenza artificiale. E allora scopriamo, definiamo e progettiamo futuri desiderabili per l’intelligenza artificiale!

2. Exploring the role of
design (thinking) in AI/ML
for WUD Rome
by Nadia Piet
@nadiapiet

3. Hi!
I’m Nadia Piet
2006
2019
Freelance service & strategic designer and
researcher with a focus on emerging and
humanity-centered tech and futures
“We shape our tools and then our
tools shape us” — Marshall McLuhan

4. Where do (service/UX)
design and AI/ML
intersect?
What’s the role of design(ers) in the
AI/ML development process?

5. aimeets.design
toolkit

6. artificial
intelligence ?

7. artificial
intelligence
the practice of making computers do things
traditionally thought of as requiring human cognition

8. artificial
intelligence
machine
learning
≄
meansgoal

9. machine
learning
programming ≄
data rules output
output
data
rules

10. A new way of communicating
with computers
Useful for problems where the
output is clear, but rules aren’t
Predictions are
probabalistic (%)

11. Prediction
Output
Cake
Model
Chef
Training
Learning
Practice
GPU
Hardware
Utensils
Algorithm(s)
Instructions
Recipe
Data
Input
Ingredients
Disclaimer: Please note this is a highly simplified representation of the actual process.
+
System
+ +

12. classification
clustering
regression

13. (semi-)intelligent
(semi-)adaptive
(semi-)autonomous
systems

14. AI will not tell us
problems worth solving or
questions worth asking or
inefficiencies worth preserving

15. human(ity)-
centered design /
design (thinking)
?

16. Where do (service/UX)
design and AI/ML
intersect?
What’s the role of design(ers) in the
AI/ML development process?

17. with of
design
AI
/ for/

18. system requirements system limitations
ML engineering space

19. user needs
system requirements
user experience
system limitations
design space
ML engineering space

20. user needs
system requirements
user experience
system limitations
design
of
design
for

21. 🤖
16 user-centric
design/engineering
considerations

22. Enabling new types
of user experiences

23. Turning tech capabilities
into user and social value
user-centered
problem solving
data-driven
opportunity spotting
tech-driven
opportunity spotting
Build on existing applications Leveraging dataResearch to application
How might AI/ML help solve
[this] in a unique way?
How might the data we
have access to create value
(for our users)?
How might we leverage
AI/ML (in processes where
good outcomes are clear
but rules aren’t)?
Developing new models

24. User research &
domain experts
for modelling
Output
(label prediction)
User experience
Input
(data sets)
Features
(factors)
Objective
(question to answer)
Business value
User
input

25. Trade-offs in
choosing an algorithm +
training a model
Precision
% of predictions that are relevant
Recall
% of objects that
are predicted
VS
How important is ..
Accuracy
% of predictions
are correct
Transparency
ability to trace back
why/how
VS

26. Benchmarking
+ evaluating
Plot:
Current human benchmark
Baseline model
Minimum confidence level
Minimum benchmark to provide value to user
100%
accuracy (?)
0%
accuracy

27. Cost of
errors
Confusion
matrix
Positive Negative
Positive :) True
positive
:( False
negative
Negative :( False
positive
:) True
negative
Machine prediction
Userreality

28. Navigating
design values
(per use case)
Emotional relationship
(‘warm tech’)
Instrumentalism
(‘cold tech’)
Automation bias /
reliance
Lack of trust /
manual
Personalization Privacy
Pro-active
(invasive?)
Re-active
(dormant?)
Human touch Computational
efficiency

29. Prototyping
the experience
(not the model)

30. Onboarding +
managing expectations

31. Explainability

32. User feedback
for machine
teaching
Data
Action
Interface
Model

33. User feedback
for machine
teaching

34. User feedback
for machine
teaching

35. User
autonomy
+ data
consent
by Philip van Allen

36. Anticipate + design
for (graceful) failure

37. Data bias
& fairness
Figure 2–5: ‘COMPAS Software
Results’, Julia Angwin et al. (2016)

38. Ethics, data privacy
& (un)intended
consequences

39. Translating subjective human experience
into computational parameters

40. user needs
system requirements
user experience /
trade-offs
system limitations
design space
engineering space
Picking +
training a model
Evaluating
your model
Cost of
errors
Explainability
User
autonomy
User feedback +
machine teaching
Bias +
Fairness
Spotting
opportunities
Expectations +
graceful failure

41. Bridging AI and design

42. “Now is our opportunity to shape that
future by putting humanists and social
scientists alongside people who are
developing artificial intelligence”
- Marc Tessier-Lavigne
President of Stanford University

43. The next design
(r)evolution
industrial economy product design
service economy service design
experience economy experience design
digital /
computational
economy
algorithm /
AI design

44. “Human-centered design has
expanded from the design of objects
to the design of algorithms that
determine the behavior of automated
or intelligent systems”
- Harry West (CEO frog)

45. 🙋
Thank you all
Grazie mille
Dankjewel
Questions?
Curious?
Ideas?
Let’s connect
@nadiapiet
hello@nadiapiet.com