We are on the verge of Artificial Intelligence (AI) exponential growth in terms of capability to perform deep learning, machine learning and big data analysis. Projections show that in the next decade AIs will likely take over jobs in a wide range of industries, covering not only manual work but intellectual one as well. What will happen in the knowledge work area? Will our work as information architects, researchers and designers be impacted, and how? While there is some discussion already around the impact of AI on the design work (https://www.oreilly.com/ideas/ai-and-the-future-of-design-what-will-the-designer-of-2025-look-like or https://uxdesign.cc/how-ai-will-impact-your-routine-as-a-designer-2773a4b1728c on Medium) less has been said around the possible changes within the design research area, with the exception of big data behavioral analysis. In this talk I’d like to explore this future scenario with a user research perspective: going through all the different phases of a research project, from the brief down to the synthesis of results, I will compare the performance of a human design researcher with that of an AI, showing strengths and weaknesses for each of them. The result of this comparison could shed light on which tasks are likely to be better achieved by AIs in the future, and where instead the contribution of a human being is still very much valuable, and why. The underlying idea is to discuss what is the human added value in our area of work, where we can take advantage of the AIs contribution, and on what, as practicioners, we should focus our careers on to ensure not only our professional survival but the best possible future for the humans we are designing for.

1. research, design, prototyping
IA vs. AI:
will robots be better researchers than us?
Raffaella Roviglioni
@raffiro
28 Sept 2018
#euroia18

2. Who am I?
10 + years of design and research experience
Currently Head of Discovery at Fifth Beat
Member of the UX/IA community in Rome, Italy and EuroIA
Raffaella Roviglioni
@raffiro

3. Why this talk?
….
Raffaella Roviglioni
@raffiro (CC) Ars Electronica Flickr

4. I am going to talk about
● A brief intro on what is AI and a
bunch of other terms
● User research phases and how
AI performs in each one,
compared to us
● So what? A few reflections and
take-aways for our future.
(CC) Carsten S.FlickrRaffaella Roviglioni
@raffiro

5. What is AI?
“Artificial intelligence (AI) is a collection of technologies that enables
machines to perform tasks that previously would require human
intelligence, such as learning, reasoning, solving problems, and
adjusting to new input.”
Cindy Lu, Alice Preston
Raffaella Roviglioni
@raffiro
(CC) Engineering in Cambrdige: Jean
de La Verpilliere, Flickr

6. Some concepts around AI
● Machine learning
● Deep learning
● Utility function
(Graymatter UK)
Raffaella Roviglioni
@raffiro

7. #euroia18
“Machine learning (ML) is the science of helping
computers discover patterns and relationships in
data instead of being manually programmed”
Josh Lovejoy and Jess Holbrook
Machine learning
Raffaella Roviglioni
@raffiro

8. #euroia18
Raffaella Roviglioni
@raffiro
(Didarc)
#euroia18
Deep learning

9. Utility function
#euroia18
“An agent's preferences over possible outcomes
can be captured by a function that maps these
outcomes to a real number”
José M. Vidal
Raffaella Roviglioni
@raffiro

10. User research and AI
Raffaella Roviglioni
@raffiro
“If you aren’t aligned with a human need, you’re just going to build a very
powerful system to address a very small—or perhaps nonexistent—
problem.”
Josh Lovejoy, Google

11. Strategy
#euroia18
● Client briefs
● Workshop to identify the goals of
the research
● Other means for collecting
business needs (stakeholders’
interviews)
Raffaella Roviglioni
@raffiro (CC) Paul L Dineen Flickr

12. Conducting research
#euroia18
In-presence interviews require a human
to interact with!
Raffaella Roviglioni
@raffiro (CC) Vincent Diamante Flickr

13. A good interviewer
#euroia18
Asks the right questions, at the right
time, following the flow of the
discussion and not imposing a script
Understands when and how to do
follow-up questions
Raffaella Roviglioni
@raffiro

14. A good interviewer/2
#euroia18
Demonstrates empathy for the person
and have them open up
Raffaella Roviglioni
@raffiro (CC) Petra Bensted Flickr

15. “I think if we allow machines to observe us long enough, they’ll
probably be able to mimic us very convincingly.
But my personal opinion is that the real emotional connection—
that real empathic connection, and the idea of being self-aware—I
think is a uniquely human thing.”
Danielle Krettek, Google Empathy Lab
Raffaella Roviglioni
@raffiro

16. A good interviewer/3
#euroia18
Observes all the non-verbal cues from
the participant to gather additional
information
Raffaella Roviglioni
@raffiro (CC) Nick Webb Flickr

17. “The chemical composition of our breath gives away our
feelings.
There's a dynamic mixture of acetone, isoprene and carbon dioxide
that changes when our heart speeds up, when our muscles tense,
and all without any obvious change in our behaviors.
It is the end of the poker face.”
Poppy Crum
Raffaella Roviglioni
@raffiro

18. A good interviewer/4
#euroia18
Follows a hunch they might have during
the interview, to explore something
they didn’t anticipate
Raffaella Roviglioni
@raffiro
(CC) flawijn .pix Flickr

19. Transcribing
#euroia18
Raffaella Roviglioni
@raffiro
(CC) DmitriyFlickr

20. - that has has even two Aussies going
- we all the way
- the way to come to Canada in but not but nothing Central African from the
Republic government those
- those three three daughters donors have had a ritual the original that they
mentioned mentioned then you then have you have other donors
- the winners mostly mostly those blues based based in in in in that the car
comp that I'm bisexual donors to
- two synagogue sitting on
Raffaella Roviglioni
@raffiro

21. Analysis
#euroia18
Relevant pieces of the transcripts are
tagged with significant keywords
Patterns are identified within the
findings
Insights emerge
Raffaella Roviglioni
@raffiro (CC) Katie Walker Flickr

22. “Deep learning can do that! By pairing lots of examples
of natural language with labels about positivity or
negativity (or any other spectrum we want to gauge),
we can develop agents that can learn to understand
the sentiments underlying new messages”
Siraj RavalRaffaella Roviglioni
@raffiro

23. Analysis
#euroia18
Coding requires background
information about the context, the
participant and the project
Input of ad hoc parameters
A clear vocabulary and paired/linked
terms
Raffaella Roviglioni
@raffiro (C) Claudio Marinangeli

24. Analysis
#euroia18
Conceptualizing our thought process is
the challenge
What determines a successful
research?
Do we know how to write a utility
function for insights?
Raffaella Roviglioni
@raffiro
(C) Claudio Marinangeli

25. Synthesis
#euroia18
Assign a priority to the insights
Understand which are actionable
Suggest the best course of action
Raffaella Roviglioni
@raffiro

26. So… what?
(CC) Antonio Esponda Flickr
Raffaella Roviglioni
@raffiro

27. Our future
#euroia18
Work with data scientists and AI
specialists
Learn how to formulate correctly utility
functions
Contribute to the collective effort as a
community
Raffaella Roviglioni
@raffiro
(CC) Raissa Ruschel Flickr

28. Will robots be better researchers than us?
Probably not.
Raffaella Roviglioni
@raffiro (CC) Ars Electronica Flickr

29. Raffaella Roviglioni
@raffiro
Raffaella Roviglioni
@raffiro
Could we become an enhanced version
of ourselves?
© Neil Harbisson

30. info@fifthbeat.com - www.fifthbeat.com
Rome, Milan and Berlin
Many thanks to
● EuroIA and the audience for this opportunity
● Claudio Marinangeli for the loving support and brain stimulation
● Fifth Beat for being my new family
keep in touch at
raffaella.roviglioni@fifthbeat.com - @raffiro