The document discusses collaborating across disciplines like data science, behavioral science, design, and product management. It provides examples of how machine learning and behavioral insights are used in products to personalize experiences. It advocates bringing these fields together to design using a transdisciplinary approach, with an emphasis on testing hypotheses, designing experiments collaboratively, and defining problems jointly. The document outlines principles for collaborating across teams and designing with human behaviors and limitations in mind.

1. Larissa Azevedo
Design, data science
and behavioural science

2. Data
Science

3. A machine that can
successfully perform any
intellectual task that a human
being can
Use of models & data to accomplish
specific problem solving or
reasoning tasks without directly
coding a solution
Machine LearningArtificial General Intelligence

5. Personalise
experiences
New ways
to interact
Provide Insights Prediction
Google now
Netflix
Chat-bots
Voice assistants
Google Photos
Identify patterns
using million of
users data
Medical scan
Anticipate users
next actions
Gmail predictive text
Value of machine learning

6. Will Ferrel and Chad Smith1 2
3 4

8. Behavioural
Sciences

9. It is the cross-disciplinary science of
understanding how people behave.
Behavioural science

10. Enable people to
make ‘better choices’
for themselves

11. Realistic model of human behaviour
Easy Attractive Social Timely
Harness the power of
defaults, send reminders,
Simplify messages.
Attract attention.
Make important
things stand out.
Design rewards and
sanctions for
maximum effect.
Use the power of
networks.
Encourage people
to make a commitment
to others.
Prompt people when
they are likely to be
most receptive.
Consider the immediate
costs and benefits.
Source: EAST framework by the UK Behavioural Insights Team

12. Urinal fly
- Reduced “Spillage”
- 75% reduction in
cleaning costs for
men’s toilets

14. Similar yet
very distinctive
disciplines that
work stronger
together
Data
Science
Behavioural
Science
User
experience
Product
Management
There is a lot of
overlap as you go deeper

15. Bringing it together

16. Walk together
Path to master collaboration between
technology, product, design, behavioural
economics, and data science teams.
How might we
design in a
transdisciplinary
manner?
Onboard
How might
we go faster?
How might we
best integrate our
teams processes?
Understand each
expertise's role
in the process
Discovery
Habit
Building
Mastery

17. Define
Develop
hypotheses
Collaboration with
Behavior Scientists
Build on
existing
processes Discover
Define the problem
space & what
behaviour we want
to influence
Collaboration
with Data
Scientists
Test
When possible,
use randomised
controlled trials to
evaluate the design
/interventions
Design
Design
Experiments
collaboratively

18. Build on
existing
processes
Use Design Sprint to kick off
work, helps the expanded cross
functional team to swarm
around a problem space

19. Build on
existing
processes
Everyone in the team
sits together

20. A common
language

23. Collaborate on
the experience
principles

24. Design
for humans
• Context matters. How we frame things and
present choices will influence their decision
• Design with human limitations in mind
(cognitive load, attention, memory, self-control)
• Humans don’t always act in their own
best interest. Don’t judge. Help to reduce
the chances of error or regret.
• Help people understand the present/future trade-offs

25. Humanise
the data
• Evolve the experience through intelligence
• Be transparent by sharing what information we’re using to
make recommendations and predictions
• Help people be the best version of themselves
recognising things that humans do really well, and use
artificial intelligence (AI) and machine learning (ML) to
support
• Build AI and ML in inclusive ways - evaluate the research
results to help prevent against incorporating biases while
training the system. Plan for how we’ll respond when - not
if - bias manifests in some unwanted way.

26. Collect data
for the future
Use vision concepts
to future identify
data required

28. Start small to
build momentum
Starting on smaller problems
together as a transdisciplinary
team we can can learn what
processes work quickly.

30. Thank you
@LarissaAzevedo
linkedin.com/in/larissaazevedo/
lazeved@gmail.com