1.
Designing Human-
Centered AI Experiences
at LinkedIn
Carolyn Chang
Christine Liao

2.
Carolyn Chang
Principal User
Experience Researcher
Christine Liao
Product Design Lead

3.
Agenda
1 Researching our own AI
2 Designing for AI
3 Shifting AI culture

4.
Create economic opportunity
for every member of the global
workforce

5.
LinkedIn AI
AI is like oxygen at
LinkedIn. Without it,
we have no good
user experience.

6.
Relevance Experience
Researching our own AI

7.
The Initial Project:
RX
Creating a user experience
point of view on LinkedIn
Relevance / AI

8.
+ =
🗑 🗑 🗑

9.
LACK OF DATA
+
😡😡😡😡😡
LACK OF ACCURACY
LACK OF CLARITY
INCORRECT WEIGHTING =

10.
LACK OF DATA
+
😡😡😡😡😡
LACK OF ACCURACY
LACK OF CLARITY
=
Data Inputs
(title, skills, industry, function,
geography, job views/applies)
Interpretation
Weighting & ranking
Output
Recommendation or result
INCORRECT WEIGHTING

11.
1
1
Lack of clarity
We don’t know what type of ‘manager’ Roxie is

12.
2
Incorrect weighting
We’re not weighing Roxie’s desired titles enough
2

13.
3
Lack of data
We don’t know whether Roxie has marketing skills
3

14.
LACK OF DATA
+
😡😡😡😡😡
LACK OF ACCURACY
LACK OF CLARITY
INCORRECT WEIGHTING
=
Data Inputs
(title, skills, industry, function,
geography, job views/applies)
Interpretation
Weighting & ranking
Output
Recommendation or result

15.
S T RAW
Designing for AI

16.
Standardized
S
T
R
A
W
Transparent
Realistic
Approachable
Worthwhile

17.
Standardized
Is this leveraging standardized inputs? And if not, how might we
capture this to help build our database?
• Add clarity to data

18.
Transparent
Are we being transparent and explaining the benefit to
members?
• Give data to get data

19.
Realistic
Is the design realistic? Are we overpromising relevance?
• Relevance humility

20.
Approachable
Does it feel approachable? How might we ensure we aren’t
overtaxing members?
• Guidance
• Examples

21.
Worthwhile
How might we ensure that every action a member takes is
worthwhile? Are we effectively using their data across all our
products?
• Leverage data
• Break data silos

22.
Building relevant products is not just
an engineering problem, it’s
everyone’s responsibility

23.
AI Empathy Workshop
Shifting AI culture

24.
3 Workshops
~150 participants across AI
engineering, product, design,
research, data science and
more.

25.
How might we...
Help AI engineers better understand
their users?
Help non-engineers better understand
our AI?
Empower teams to make better AI
decisions?
Build deeper relationships between AI
& Design/UXR?
Make this fun?

26.
The Exercise

27.
Participant Video

28.
“Profile has two jobs.
Represent the member
accurately and feed our
relevance”
Product manager

29.
Impact

30.
4.8/5
Average rating of workshop
Perception shift.
Helped engineers, product, etc.
understand the mindset, goals and
needs of users.
Many learned things they never
thought of before.

31.
“We aren't the user and thinking
from POVs of diverse users is
important to improve relevance.”
AI Engineer

32.
“I have a lot more empathy for our
members and feel more inspired
and motivated to help solve some
of these problems”
Product manager

33.
“Relevance is hard! Work with
relevance [engineers] earlier
in the design process.”
Designer

34.
Create the best human-centered AI
experiences that help us achieve
LinkedIn's vision

35.
Thank you

36.
Abstract
Are you trying to understand AI at your organization? Looking for ways to connect with engineers?
Hoping to design human-centered AI experiences for users?
This talk will cover:
1. LinkedIn AI and the importance of quality data
2. A framework on how to design human-centered AI experiences
3. Cross-functional workshops that help engineers and other functions understand user perceptions
of AI