This document summarizes Carolyn Chang and Christine Liao's talk on designing human-centered AI experiences at LinkedIn. It discusses (1) researching their own AI systems to understand issues like lack of data, accuracy, and clarity, (2) establishing a framework called STRAW to design AI with standardized, transparent, realistic, approachable, and worthwhile experiences, and (3) shifting AI culture through cross-functional workshops that helped engineers and others build empathy for users and their perceptions of AI. The workshops received positive feedback about improving understanding between teams.

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

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

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

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

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

9. Relevance Experience
Researching our own AI

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

12. + =
🗑 🗑 🗑

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

14. 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

15. 1
1
Lack of clarity
We don’t know what type of ‘manager’ Roxie is

16. 2
Incorrect weighting
We’re not weighing Roxie’s desired titles enough
2

17. 3
Lack of data
We don’t know whether Roxie has marketing skills
3

18. 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

19. S T RAW
Designing for AI

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

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

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

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

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

25. 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

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

27. AI Empathy Workshop
Shifting AI culture

28. 3 Workshops
~150 participants across AI
engineering, product, design,
research, data science and
more.

29. 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?

31. The Exercise

32. Participant Video

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

34. Impact

35. 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.

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

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

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

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

40. Thank you

41. 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