racist language and became obnoxious
in under 24 hours.
How about Google Photo? The
creator may not have used a diverse
enough dataset to train the model.
Or they have not considered all
predictable use cases and have not
tested enough to uncover likely
What can we learn from such
• Embrace human nature and design
with all of humanity in mind.
• Testing, testing—always testing.
The simpler, the more complex.
What is the best part of AI? It makes
things simpler. Voice makes typing
faster; search-engine algorithms
help you to find whatever you want
in seconds. Smart e-commerce
recommendations know what you like.
How come it is so simple? Because
it is complex. The simpler it is to a
human, the more complex it is to the
machine. Take voice typing. It looks
simple—voice input and transcription
output. It looks like the user does not
have any interactions with the system
except the input. In fact, the user and
the system are interacting with each
other all the time:
User turns on the mic… System
starts listening… User starts speaking…
System is gathering user’s input data…
System starts transcribing… System
finishes transcribing… System shows
the transcription… User pauses for a
moment… System is still listening…
System may stop listening if there is
no input for a while… User resumes
speaking… System is listening… User
corrects the transcription…
“Send.” The whole experience
ends with this action. There are
other use cases of course: What if
the user speaks too fast? What if the
environment is too noisy?
Think deeply and do not be
fooled by the surface. Look into the
experience of the user as well as that of
the system. Since we are designing the
interaction, we’d better pay attention
to both sides to reveal the pain points
and improve the whole experience.
Behavior change is hard. We can
all be lazy. We tend to choose the
default to avoid costs and risks (aka
the “default effect”). Most users use
the default keyboard that comes with
their phones. “It is good enough.”
“I never thought about looking for
Behavior change is hard but it is
not impossible. It should better serve
users’ most urgent needs and guide
them to a new and better experience.
Traditional keyboards provide an
entrance for users to access the voice
input. Unusually, voice input is the
default on Talk Type (Figure 1). We
put the mic button up front. Users
become more familiar with voice
input thanks to its strong exposure.
And there is a greater chance that
users will tap the mic button and
speak! Users also have ready access
to the regular keyboard and shortcut
keys such as numbers and emojis.
MORE THAN UI DESIGN
If I am alone, I may use it. But if it is in
public, it is too weird.
What do you mean by “weird”?
Isn’t it weird to speak to your phone?
And the phone will not speak back to you?
What’s odd about artificial
intelligence is that it is artificial. It
cannot be more natural than speaking
with an actual person. But it is weird to
speak to something artificial, though it
should involve the same behavior. How
can we make this odd experience more
natural and user friendly? We need to
find out the “why.” We need to know
the user as well as the machine. We
need to study not only the technology
and the design, but also the sociology
and the humanity.
Two of many researchers who have
studied this relationship between
humans and technology are Clifford
Nass ( https://en.wikipedia.org/wiki/
Clifford_Nass) and Sherry Turkle
( http://www.mit.edu/~sturkle/). I
admire them immensely.
This relationship involves more
than design, and far more than UI
SO, YOU WANT TO BE
AN AI DESIGNER?
You enrolled in several machine-learning courses on Coursera. You read
every news article about AI. You bought
an Amazon Echo and a Google Home.
You want to be an AI designer. You ask
First, ask a lot of whys: Figure out
your inner desire. Why do you want
to work on AI? Just because it sounds
DOI: 10.1145/3106743 COP YRIGHT HELD BY AUTHOR. PUBLICATION RIGHTS LICENSED TO ACM. $15.00
cool? Or do you believe it can change
Just try: Try to use products that
make good or bad use of AI; try to
think about problems that AI can
help to solve. And talk to people—AI
experts, creators of AI-enabled
products, users who like and are using
those products in daily life. Then ask
yourself a lot of whys again until you
find the root cause of your want.
Learn smart, not hard: Knowing
how it works is more important than
learning how to do it. Our goal is not
to be an AI researcher or engineer,
but rather to design better AI-enabled products.
Collaboration is the key. It’s
the beginning of the relationship
between the human and the machine.
There are a lot of challenges as well
as opportunities. It requires AI
researchers, software engineers, and
product designers to collaborate and
build AI-enabled products that can
solve existing problems and create new
1. Blaase, N. Why product thinking is the
next big thing in UX design. 2015; https://
2. See full response at https://www.quora.
3. Knight, W. Powerful speech technology
from China’s leading Internet company
makes it much easier to use a smartphone.
4. CB Insights. The rise of bots: A timeline
of major VC-backed bot startups; https://
5. Cohen, D. Here are the new features
available to Facebook Messenger chat bot
developers. 2017; http://www.adweek.
6. Pathak, S. Drop it like it’s bot: Brands
have cooled on chatbots. 2017;
Nina (Zhuxiaona) Wei is a product designer
at an AI lab who has a great passion for
products, psychology, and the relationship
between humans and technology.
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