Big Data, Big Design: Why Designers Should Care about Artificial Intelligence

177 Pages·2021·76.97 MB·English

by  Helen Armstrong

Princeton Architectural Press, New York
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Book Designer: Helen Armstrong
Illustrator: Keetra Dean Dixon Ellen Lupton
Research Assistants: Isabel Bo-Linn and Eryn Pierce
Editors: Jennifer Thompson and Kristen Hewitt, Princeton Architectural Press
Typography: Chercán, designed by Francisco Gálvez in 2016
Library of Congress Cataloging-in-Publication Data
Names: Armstrong, Helen, 1971-author. | Dixon, Keetra Dean, illustrator.
Title: Big data, big design : why designers should care about AI / [edited by]
Helen Armstrong; with illustrations by Keetra Dean Dixon.
Description: First edition. | Hudson, New York : Princeton Architectural Press, 2021
Includes bibliographical references and index.
Summary: “Big Data. Big Design. (BDBD) demystifies machine learning (ML)
while inspiring designers to harness this technology and establish leadership
via thoughtful human-centered design”—Provided by publisher.
Identifiers: LCCN 2021006603 | ISBN 9781616899158 (paperback) |
ISBN 9781648960789 (epub)
Subjects: LCSH: Product design—Data processing. | Design—Data processing.
Computer-aided design. | Designers—Interviews. | Artificial intelligence. | Big data.
Classification: LCC TS171.4 .B524 2021 | DDC 658.5/752—dc23
LC record available at https://lccn.loc.gov/2021006603
Helen Armstrong, a professor of graphic design at
North Carolina State University, focuses her research on
accessible design, digital rights, and machine learning.
Armstrong is the author of Graphic Design Theory: Readings
from the Field and Digital Design Theory: Readings from
the Field, and she is the coauthor of Participate: Designing
with User-Generated Content.
Contents
04 Acknowledgments
05 Preface
07 Chapter One: Peek Inside the Black Box
32 John Zimmerman, PhD, Carnegie Mellon University | Interview
34 Joanna Peña-Bickley, Amazon | Interview
36 Rebecca Fiebrink, PhD, University of the Arts London | Interview
38 Alex Fefegha, Comuzi | Interview
40 “Animistic Design,” Philip van Allen, ArtCenter College of Design | Essay
44 “Machines Have Eyes,” Anastasiia Raina, Lia Coleman,
Meredith Binnette, Yimei Hu, Danlei Huang, Zack Davey, Qihang Li,
Rhode Island School of Design | Essay
53 Chapter Two: Seize the Data
78 Silka Sietsma, Adobe | Interview
80 Pattie Maes, PhD, Massachusetts Institute of Technology | Interview
82 Patrick Hebron, Adobe | Interview
85 Stephanie Yee, Stitch Fix; Tony Chu, Facebook | Interview
88 “Thinking Design + Conversation,” Paul Pangaro,
Carnegie Mellon University | Essay
94 “More than Human-Centered Design,” Anab Jain, Superflux | Essay
101 Chapter Three: Predict the Way
128 Rumman Chowdhury, PhD, Parity | Interview
130 David Carroll, Parsons School of Design | Interview
132 Caroline Sinders | Interview
134 Sarah Gold, IF | Interview
136 “What Is Missing Is Still There,” Mimi Ọnụọha | Essay
142 “ Anatomy of an AI System,” Kate Crawford and Vladan Joler,
AI Now Institute | Essay
149 Chapter Four: Who’s Afraid of Machine Learning?
163 The Future: Exciting but Fraught | Conclusion
167 Notes
174 Credits
175 Index
Acknowledgments
My initial interest in machine learning (ML) sprang from my desire to use
this technology to design individualized experiences for my special-needs
kiddo. Technology has failed to meet the needs of a large swath of the pop-
ulation. ML can help provide access and meet those needs—or it can amplify
marginalization. We stand before both possibilities.
Technology’s failures stood out starkly during the Covid-19 pandemic, a
period during which the bulk of this text took form. Special acknowledgment
to all the parents who, like me, spent the pandemic running back and forth
between their laptops and their kids’ laptops—particularly the special needs
parents who had to adapt everything on the fly so that their children might
Design contiIn, ume ytsoe llef,a wrno. uld not have survived without the support of my partner,
Big Data, Big SVeivaina nK aranuds Tee, sasn.d W thiteh ptohseiitri vhee lspp, itrhiti sa nbdo ocka nc-admoe a ttoti ftruudieti oonf .m Iny atwddoi tcihoinld, ren,
4 | thank you to my wonderful colleagues at North Carolina State University for
their continuous inspiration and support. Thank you Denise Gonzales-Crisp,
Deborah Littlejohn, and Matt Peterson for all the Zoom happy hours and
emergency text chains. Special thanks to Tsai Lu Liu for his leadership.
I would also like to recognize all the wonderful students in our master’s
program in graphic design who provided a strong sounding board for this
text, particularly my research assistants, Isabel Bo-Linn and Eryn Pierce.
Essential to this project were, of course, the many designers,
researchers, and data scientists who graciously contributed to the book
through interviews, essays, and projects. Your work inspires and delights,
sketching out wonderful possibilities and essential guardrails for ML.
Thanks, as well, to my industry collaborators over the years from SAS
Analytics, IBM Watson Health, Advance Auto Parts, and many others. And
special thanks to Keetra Dean Dixon for her amazing illustrations for this
project. At Princeton Architectural Press, a special shout out to Jennifer
Thompson and Kristen Hewitt for their thoughtful comments and ongoing
support of the project. Working on this book has been a joy. So many possi-
bilities lie before us in the coming years. Let’s, together, grasp the ones
that will lead our society forward.
P reface
“ Be kind to each other. Because every action you make
is what creates the future.”—Mother Cyborg
Why should a designer care about machine learning (ML)? Fair
question, right? After all, what do algorithms and predictions have
to do with you? The answer grows more self-evident by the day.
Artificial intelligence (AI) is everywhere and has already trans-
formed our profession. To be honest, it’s going to steamroll right
over us unless we jump aboard and start pulling the levers and
steering the train in a human, ethical, and intentional direction. Design
Hbye rtea’ps painnogt thheer areliaesno pno ywoeur ssh oofu nldo nchaurem: ayno uc ocgann itdioo na.m Tahzininkg o wf oMrkL Big Data, Big
as your future design superpower. Oh, and one last thing: industry 5 |
and academia alike prize designers with a full understanding of
this technology.
So, we have some studying to do. Together, we are going
to take a journey. A journey across the three realms of ML. Each
section considers ML and design through the lens of a central
essay, a series of interviews, and several miniessays from a range
of contributors. Want to break off from the path to dig deeper
into how predictive algorithms work? An additional, more tech-
nically focused chapter follows these main sections. The book
concludes with a short essay addressing the impact of ML upon
design practice itself. In other words, in addition to the
impact upon what we make, how might ML affect how we make?
Hopefully, this book will inspire you to take hold of ML, care-
fully but confidently. We should not trust a technology that has
no true understanding of human consequences to take the lead.
Instead, we human designers have to blaze the path forward
ourselves. Let’s get started.
Author’s note: The development of artificial intelligence has a long,
complex history. The main branch of AI in use today is machine learning—
an approach to AI explained throughout this book. This text uses the
terms AI and ML interchangeably with this in mind.
CHAPTER ONE
P eek Inside
t he Black Box
Each day we generate data—terabytes of it. How
have you produced data in the last month? In the
last week? In the last hour? Did you write an email?
Post a photo? Text a friend? Watch a streaming
video? Wear an activity tracker? Drive through a
traffic camera? As we move through our lives, we
leave behind a garble of unstructured data—i.e.,
data not organized into ordered sets like spread-
sheets or tables. Scholars claim that as much as 95
percent of all data is unstructured.1 Machine learning
(ML) enables a computer to derive meaning from
all this unstructured data. Even now as you read,
computers sift and categorize your data trails—
both unstructured and structured—plunging deeper
into who you are and what makes you tick.
CHAPTER ONE
P eek Inside
t he Black Box
Each day we generate data—terabytes of it. How
have you produced data in the last month? In the
last week? In the last hour? Did you write an email?
Post a photo? Text a friend? Watch a streaming
video? Wear an activity tracker? Drive through a
traffic camera? As we move through our lives, we
leave behind a garble of unstructured data—i.e.,
data not organized into ordered sets like spread-
sheets or tables. Scholars claim that as much as 95
percent of all data is unstructured.1 Machine learning
(ML) enables a computer to derive meaning from
all this unstructured data. Even now as you read,
computers sift and categorize your data trails—
both unstructured and structured—plunging deeper
into who you are and what makes you tick.
Box
Black
Peek Inside the
8 |
FIG 1. STITCH FIX ALGORITHMS TOUR.
Through interactive storytelling,
Stitch Fix visualizes its use of rich
data to match clients with items
of clothes, shoes, and accessories.
The company combines algorithmic
decision making with human skills—
intuition, understanding context,
and building relationships—
to make shopping personal.
Today, computers intuit the world more like humans.
When I enter a room, I don’t learn about the room via a
spreadsheet. Instead, I use my senses. I analyze images,
sound, space, and movement. I take this information and
make decisions based on what I find. Combining sensors
MACHINE LEARNING
(ML): process of (accelerometers, barometers, gyroscopes, proximity sensors,
using algorithms
heart rate monitors, iris scanners, ambient light sensors,
to identity patterns
from data and then chemical and microbial sensors, electric noses) and other
make predictions or
input devices (cameras, microphones, touch screens) with
determinations about
the world without ML turns each trail of unstructured data into a richness of
eUdfhdoxaaaNrpttvSmaaelTi caimRanit tU op itptChdrsreTea onUdltg eaRodrtfirEoai nveDimsees D md n nA ooiTnttA g : otysrotraogunrares nddfioaz, ertamadn—,id nc igoan nvtvoeaat lsiyentzd feqo ddura.ma tYnaaott iuritoerie nspso ot uohlirftac itpec rssce,.a vynImio obuauergs lpidynee eurt snetohcunetsea aidlbmi,tl epyd, ai dgcaittt aoal—fl y Peek Inside the Black Box
organized in a pre- your sexuality, your next move. This is the future that is 9 |
determined manner
materializing right now.2
STRUCTURED DATA:
organized data that Without the sheer quantity of this data—data that
follows a standard-
used to be lost in the digital abyss—ML could not function
ized format
effectively. Why is this? ML algorithms train using examples—
bundles of data. The size and range of the examples deter-
mine the subsequent accuracy rate.3 This training process also
requires masses of “compute,” i.e., resources and processing
power to fuel complex computation. According to journalist
John Seabrook, “Innovations in chip design, network architec-
ture, and cloud-based resources are making the total available
compute ten times larger each year—as of 2018, it was three
hundred thousand times larger than it was in 2012.”4 ML has
taken off recently because both data collection and compute,
along with accessible and affordable input devices and
sensors, now flourish in our society.
THE ONSLAUGHT OF ALGORITHMS
But how does this mysterious ML stuff really work?
Yesterday I checked my email, searched for an old high school
friend online, used Waze to get across town—and tip me
off to where the cops were—checked my Instagram feed,