1.
Progressive
Design with AI
GIDEON SIMONS - DEC 2021 @ UXSTRAT

2.
Gideon Simons
INSPIRATIONAL QUOTE:
SR. DIRECTOR OF PRODUCT DESIGN AND UX RESEARCH @ ZINIER
PREVIOUSLY
UXR Agency, Innovation Lab, Mediacorp, IBM, SeeSaw, CTO x2
“I'd rather have a bottle in front of me than a frontal lobotomy” -
Tom Waits
EMOJI
🦄

3.
It’s 2018, robots haven’t taken my job.. phew! my
mirror is still dumb, drones are not delivering my
packages and the taxi I took had a chatty human
driver.
From a talk I gave 3 years
ago at UXSEA conference!

4.
Designers Don’t
be afraid of AI!

5.
AI has a lot of fancy
terminology such as
‘confusion matrix’ which
can be intimidating and
also… a bit confusing..

6.
In this talk you will learn
an easy framework for
designing amazing
experiences with both
dumb and smart AI

7.
Progressive design with
AI basically means that
the user experience
grows together with AI
getting better.
At the heart of the
framework are these two
important factors.
AI Performance
How and when to use the AI
AI Confidence
What do you do with the prediction
THE FRAMEWORK

8.
Let’s start with AI Performance

9.
AI Performance
is how well can the AI
detect True Positives
and False Negatives
(on your data)

10.
It’s a Lion
False - Positive
It’s a Lion
True - Positive
Isn’t a Lion
True - Negative
Isn’t a Lion
False - Negative
Confusion Matrix explained with cats and lions

11.
As good as
flipping a
coin!
50% - 85%
below 50%
75% and
Above
Let’s do it
above 85%
Unreliable
Acceptable
Performance Score (F1) Value Scale
* Note that these are rule of thumb numbers and may vary between use cases

12.
Performance Score is 89%
The AI Model can predict task demand and manpower capacity with 89% accuracy!

13.
Performance Score is 70%
Which isn’t very reliable but maybe still useful as a softer insight

14.
Performance Score is 44%
AI is completely useless but we still want to give something useful!

15.
Next is AI Confidence
AI Performance
How and when to use the AI

16.
AI Confidence
Is how confident AI is
about a given prediction
(on a new input)

17.
Confidence Score explained with a lion cub
App
AI
App
AI
How confident are
you about that?
Is this a Lion?
I am about 86%
confident that
this is a Lion..
And about 35% that it’s a
cat..
And about 5% that it’s a
coffee mug..
Hmm… yes!
* Tip - If your AI model doesn’t have this out of the box then work with devs and data scientists to create one together

18.
Predicted No
25% - 85%
below 25%
75% and
Above
Let’s do it
above 85%
Not sure!
Predicted
Yes
Confidence Score Value Scale
* Note that these are rule of thumb numbers and may vary between use cases

19.
Confidence Score is 89%
AI is confident that this is an Antenna and so we can help nudge the reviewer to Pass!

20.
Confidence Score is 52%
AI is not very confident, lets ask the user to help teach the AI

21.
Confidence Score is 12% for Cabinet
AI is doing a good job telling you that the thing in the photo isn’t a Cabinet!

22.
And Lastly - Some more
helpful guidelines
AI Performance
How and when to use the AI
AI Confidence
What do you do with the prediction

23.
Gain trust from users
Progressively!
Not immediately..

24.
Don’t Overfit solutions
Like what 80% of all blockchain startups do 😁

25.
Be Ethical!
No life-death use cases
Avoid putting some groups of people
at a disadvantage to others

26.
Acknowledge biases..
Don’t be afraid to let your users know about your
weaknesses and ask users for help!

27.
“When AI solves the wrong thing, is built
on faulty data, operates with lack of
purpose, is misaligned with people’s needs,
creates no room for feedback, doesn’t
consider context, oversimplifies nuance, it
is likely to fail, and worse, to harm.”
- Jennifer Comiskey
From: The Future of AI is People-Centered - which you can find on Medium
Super 3d illustrations by
Alzea Arafat!

28.
Questions? 󰢧󰢨🙋

29.
Thank You!