This session covers three key principles for how design and data science teams can work together better to build greater trust among users. Additionally, a case study on how a design and data science team partnered to redesign predictive analytics scores powered by machine learning will illustrate those principles in practice. Por Crystal Yan

1. Behind the AI Curtain:
Designing for Machine Learning Products
#aicurtain
Crystal Yan

2. I’m Crystal Yan
I’m a designer and product manager who works
with data scientists every day.
You can find me at @crystalcy
Hello! ✋
@crystalcy | #aicurtain
crystalcyan.github.io

3. Terminology Cheatsheet
Data Science
Getting insights from
data
(everything from business
analytics and statistics to
machine learning)
Artificial Intelligence
Machines have
intelligent behavior
(goals and methods include
machine learning, natural
language processing,
computer vision, facial
recognition)
Machine Learning
Computers learn on
their own without
explicit programming,
requires lots of data
(ML is a method that can be
applied to create models that
will predict, aka predictive
analytics)
@crystalcy | #aicurtain
crystalcyan.github.io

4. Today’s Agenda
Introduction
Why this matters
Principles
1. Less is more
2. Ask the right
questions
3. Writing well
matters
Case Study
Redesigning
predictive analytics
scores
@crystalcy | #aicurtain
crystalcyan.github.io

5. Artificial intelligence is
changing the
It’s everywhere, whether you see it or not. We interact with more
systems powered by AI each day.
I work with data scientists and often meet designers and clients who
ask, “Are algorithms here to take my job?! ”
1
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crystalcyan.github.io

6. The New Yorker
What happens when
machines out-diagnose
doctors?
From medicine to SaaS
VC blogs
Will your users trust your
analysis / will they pay for it?
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crystalcyan.github.io

7. “
“Artificial intelligence will have reached human
levels by 2029. Follow that out further to say,
2045, we will have multiplied the intelligence, the
human biological machine intelligence of our
civilization a billion-fold.”
-Ray Kurzweil
Inventor
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8. “
“The field of AI has traditionally been focused on
computational intelligence, not on social or
emotional intelligence. Yet being deficient in EQ
can be a great disadvantage in society.”
-Rana el Kaliouby
Cofounder of Affectiva
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9. “
“We all have a responsibility to make sure
everyone - including companies, governments
and researchers - develop AI with diversity in
mind.”
-Fei-Fei Li
Professor at Stanford University and Director of AI Lab
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10. Three Principles
Here are three principles for establishing greater trust in the machine
learning behind your design.
2
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11. Principle #1:
Less is more.
Sometimes...it pays to hide the numbers. @crystalcy | #aicurtain
crystalcyan.github.io

12. Principle #2:
Ask the right questions
Just right:
How would you
explain this?
Too leading:
Does this make
sense?
Do you like this?
Too
open-ended:
What would you
do? What would
you call this?
Most importantly: listen to their questions. @crystalcy | #aicurtain
crystalcyan.github.io

13. Principle #3:
Writing well matters.
◉ Define your audience and purpose.
◉ Set tone/personality and match to brand.
◉ Be concise. Solution first, evidence after (for those who seek it).
Good writing is concise, scannable, objective, and actionable.
Resources: plainlanguage.gov, Letting Go of the Words (book)
@crystalcy | #aicurtain
crystalcyan.github.io

14. Redesigning Predictive
Analytics Scores
Case Study: FiscalNote
3
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15. test
Our design process
dev &
release
iterate
define
problem
ideate/
prototype
@crystalcy | #aicurtain
crystalcyan.github.io

16. We promised the world.
But people had trouble
understanding this score,
and the company strategy
changed.
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17. But...why?
The to the
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18. The redesign.
We hid the numbers
We gave an explanation
We adopted a
conversational tone
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19. $MONEY
a lot of revenue at the time attributed to predictive analytics
140+
number of training docs we created on our internal drive to try to
explain the scores
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20. 5/5
everyone could concisely articulate how they would explain the new scores
to a coworker
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21. Recap
Less is more
In our case, it made sense to
hide the numbers.
Ask the right questions
What you ask defines what
you’ll get. Listen for insights from
the questions users ask.
Writing well matters
Brush up on your writing skills,
or risk getting left behind.
Adapt
Algorithms might not take your
job, but you must adapt.
Why matters more than what
People wanted to know why we
gave a particular score. In
general, they preferred a less
accurate human analyst over a
more accurate black box.
Copy > graphics
Our redesign shifted focus from
charts to copy.
@crystalcy | #aicurtain
crystalcyan.github.io

22. Any questions?
You can find me at
◉ @crystalcy
◉ crystalcyan.github.io
Thanks!
@crystalcy | #aicurtain
crystalcyan.github.io