Debapriya Basu is a Principal Product Manager at Zillow who specializes in AI products and technology. The presentation covers designing user experiences for AI-infused systems, including principles of design such as embracing customer needs and building trust. Guidelines are provided for user interaction stages like before first interaction, during interaction, and when things go wrong. A framework is outlined for the design process, including determining how AI can help, identifying user needs, collecting and analyzing data, designing prototypes, and incorporating feedback.

1. DESIGN UX FOR AI
DEBAPRIYA BASU
PRINCIPAL PRODUCT MANAGER @ ZILLOW

2. HELLO!
I am Debapriya Basu
I specialize in AI products and technology
You can find me at
https://www.linkedin.com/in/debapriyabasu/
Current: PM@Zillow Group
Former: PM@Microsoft
Previous Product School talk: Lead AI (ML) incubations as a PM
2

3. WHAT I WILL
COVER
Design for AI infused systems
◎ Principles of design
◎ Guidelines for user interaction stages
◎ Framework of design process
3

4. PRINCIPLES OF
DESIGN
4

5. EMBRACE
CUSTOMER
NEED
◎ Need : long (sometimes painful) process to
◎ learn how much is a home worth in current
local markets
◎ Zestimate
◎ Started as home estimate for next one year
◎ Updated to reflect quality of home (using
computer vision)
5
ZESTIMATE

6. BUILD TRUST ◎ Is it well intentioned
◎ Does it perform the way we envisioned
◎ Is it safe to use. Is it correctly built
6
ref

7. ETHICAL AI Privacy
AI models should be built within
guardrails to ensure users cannot be
identified or their details inferred
from model output
Researchers were able to identify Netflix
users by correlating anonymized test data
provided in the Netflix Prize competition with
publicly available IMDB movie review
database.
7
Fairness
AI/ML models used for making decisions or
predictions should not be biased with respect
to protected attributes (latent bias) such as
race, gender and sexuality. It should be aware
and counter impacts of interaction and
selection bias also.
A MIT study Project Gender Shades uncovered
the bias that facial analysis technologies have
a heavy bias towards white males.
JoyAdowaa Buolamwini foundedAlgorithmic
Justice League, an organisation that looks to
challenge bias in decision making software

8. AMPLIFY HUMAN
CAPABILITIES
WITH AI
◎ Google Pixel Buds integrated with Google assistant
◎ Personalized recommendations
◎ Automated personal communication
■ (Smart Reply)
◎ offloads monotonous tasks
■ Set timers
■ Control IoT devices
8
GOOGLE ASSISTANT
here

9. GUIDELINES FOR
USER INTERACTION
STAGES
9

10. STAGES OF
USER
INTERACTION
◎ Before first interaction
◎ During course of interaction
◎ When things go wrong
◎ Over continued repeated interaction
10

11. BEFORE THE FIRST INTERACTION
11
Clarify what the system can do Clarify how well the system can do what
it promises to do
ref
ref

12. DURING THE INTERACTION
12
Show contextually relevant insights Match relevant social norms
Mitigate social bias
Time services based on context

13. WHEN THINGS GO WRONG
13
Scope services when in doubt Clarify what the system did
Allow efficient correction
Allow efficient dismissal
Allow efficient invocation
UX has a critical role to play here

14. OVER CONTINUED REPEATED INTERACTION
14
Remember recent interactions
Update and adapt cautiously Encourage granular feedback
Learn from user behavior

15. OVER CONTINUED REPEATED INTERACTION
15
Notify users about changes
Provide global controls
Convey consequences of user action

16. PM-DESIGNER
FRAMEWORK FOR
DESIGN PROCESS
16

17. STAGES
17
Determine
how AI can
help
Identify
user
needs
Train ModelsCollect
Data
Test prototypesDesign to
meet user’s
mental model
Build
Trustworthy
AI
Explain
AI
Handle
failures
gracefully
Incorporate
Feedback

18. IDENTIFY
USER NEED
+
HOW CAN AI
HELP
RESEARCH
Understand the user
need through user
research, opportunity
analysis, competitive
analysis
OBJECTIVE OR REWARD
FUNCTION
What kind ofAI technology
will help solve. What is to be
prediction or concluded
18
EXPERIENCES
Identify hero
experience to ideate
SUCCESS
DEFINITION
Define success in terms
of online metrics
ALIGN
Align stakeholders to a
common understanding
of the user problem and
proposedAI infused
experience

19. COLLECT DATA
+
TRAIN MODELS
DATA
◎ 1st Party vs 3rd Party
◎ Privacy and Security
◎ Fresh
◎ Unbiased
◎ Representative
◎ Relevant
◎ Missing data
◎ Noisy data
◎ Inconsistent
19
AI TECH
◎ Machine learned
◎ Supervised
◎ Unsupervised
◎ Reinforced
◎ Modeling Techniques
◎ Deep learning
◎ NLP
◎ Ensemble Learning
◎ Pre-trained Models
DESIGN FOR LABEL GENERATORS
Accommodate diversity of raters and their context and incentives

20. DESIGN TO
MEET USER
MENTAL
MODELS
UNDERSTAND MENTAL MODEL
◎ User group and their primary goal,
◎ novice user’s preferred flow,
◎ expert user’s preferred flow,
◎ current mental model,
20
INFLUENCE MENTAL MODEL
◎ Clarify future mental model,
◎ Create bridge from current to
future
◎ explain cause and effect ofAI
model

21. TEST MENTAL MODEL
◎ Onboard users
◎ Ask users what they thought the
product will do
◎ Study their reactions to
wireframes/prototypes
◎ Learn about their expectations
about using theAI experience
21
PRODUCT
◎ Guerilla testing
◎ Wizard of Oz testing

22. 22
TRANSPARENT + EXPLAINABLE + TRUSTWORTHY AI
MODEL CONFIDENCE
DATA SOURCES
MODEL
FEATURES
FAIRNESS

23. INCORPORATE
FEEDBACK
IMPLICIT (critical forAI)
◎ Subscribed to channel (+)
◎ Add to playlist (+)
◎ Add to watch later (+)
◎ Shared video (+)
◎ Watched till the end (+)
◎ Bounced off after few secs (-)
◎ Clicked on a related recommendation after
few secs on video (-)
◎ Watch videos in bits and pieces (-)
23
EXPLICIT

24. DEGRADE
GRACEFULLY
IN CASE OF NON HAPPY PATH RESULTS OR SYSTEM FAILURES
◎ Category suggestions
◎ Alternate queries
◎ Personalized recommendation
◎ Helpful links
◎ Popular products
◎ Popular categories
◎ Contextual ads
24

25. WHAT I
COVERED
Takeaways for AI powered product design
◎ What are the cornerstone principles
◎ How to design for user interaction stage
◎ What is the framework for the design process
Please reach out with your comments and thoughts at
tutulpriya@gmail.com
25

26. REFERENCES ◎ https://pair.withgoogle.com/guidebook/
◎ https://www.microsoft.com/en-us/research/uploads/prod/2019/01/Guidelines-for-Human-
AI-Interaction-camera-ready.pdf
◎ https://www.media.mit.edu/projects/gender-shades/overview/
◎ https://medium.com/myplanet-musings/10-ui-patterns-for-a-human-friendly-ai-e86baa2a4
471
◎ https://uxdesign.cc/guidelines-for-infusing-artificial-intelligence-to-products-bbbcf7b928f
7
◎ https://becominghuman.ai/ux-design-for-implicit-and-explicit-feedback-in-an-ai-product-
9497dce737ea
◎ https://techcrunch.com/2018/12/07/google-translate-gets-rid-of-some-gender-biases/
◎ https://www.nngroup.com/articles/recommendation-guidelines/
◎ https://www.pcworld.com/article/3393186/microsoft-ideas-words.html
◎ https://baymard.com/blog/no-results-page
◎ https://www.blog.google/products/assistant/stay-top-your-day-proactive-help-your-assistant/
26