World Usability Day, 2018 AI is becoming a greater part of the systems and products we design, yet algorithms have been shown time and time again to be imbued with unintentional racism, sexism, and other -isms. As design and AI fields converge can how researchers, designers, and developers work together to ensure that our powers are used for good, and not for accidental evil?

1. Design + AI + “_isms”
World Usability Day 2018
Michael Zarro

2. About Me
Michael Zarro, PhD
Research & Design
This work was prepared by the author in a personal capacity. The opinions expressed in this article or presentation
are the author's own and do not necessarily reflect the view of any employers, sponsors, colleagues, or band mates
past, present, or future.

3. Design?
Design defined by Meriam
Webster:
• to create, fashion, execute, or
construct according to plan
• to devise for a specific
function or end
• to make a drawing, pattern, or
sketch of
• to draw the plans for

4. Artificial Intelligence (AI)?
“Artificial intelligence is technology that appears to emulate
human performance typically by learning, coming to its own
conclusions, appearing to understand…”
https://www.gartner.com/it-glossary/artificial-intelligence/
“Five years ago, I was interviewed by a reporter that said ‘how
come AI is such a failure’…Today, they say ‘AI is going to take
over the world and kill all humans or steal all our jobs?’”
- Peter Norvig, Google
"It's happening anyway, you may as well try to influence it"
- Katie Kelsall

5. _isms ?
• Racism
• Sexism
• Ageism
• ___
Source: https://ethw.org/File:Hopper.jpg

6. Grace Hopper
A partial list of accomplishments:
• Bachelor’s degree in mathematics and physics
from Vassar College (Phi Beta Kappa)
• Master’s and doctorate in mathematics from
Yale in 1930 and 1934.
• Bureau of Ordnance Computation Project during
WWII
• One of the first computer programmers ever
• US Navy Rear Admiral
• National Medal of Technology 1991
• USS Hopper, a guided missile destroyer named
for herSource:
https://ethw.org/Grace_Murray_Hopper

7. The first person to identify a literal computer bug.
Source:
https://www.phillyvoice.com/70-years-ago-six-
philly-women-eniac-digital-computer-
programmers/

8. https://cacm.acm.org/news/231814-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women/fulltext

9. https://techcrunch.com/2016/03/24/microsoft-silences-its-new-a-i-bot-tay-after-twitter-users-teach-it-racism/

10. Let’s
talk
waves
Image source: https://www.huffingtonpost.com/entry/-world-dog-surfing-championships_us_5b67c3fee4b0de86f4a2cfe7

11. Wave 1
AI (Per DARPA)
• Machines follow rules in
well defined domains
• Planning tools and expert
systems
• If this, then that….
• No learning or abstracting
Design (HCI)
• “The Desktop” as a
metaphor for using
computers
• Ergonomics
• Engineering
• Research and design
centered around tasks
https://www.darpa.mil/attachments/AIFull.pdf

12. Wave 2
AI (Per DARPA)
• Statistical models used to
train algorithms, with limited
understanding of context or
reasoning.
• Neural Nets and Big data
• Processing, storage, and
telecommunication
technologies support
powerful systems
• Little Abstract and reasoning
Design (HCI)
• Computers and humans
working together as
information processors
• Focus on interactions and
processing
• Research and design based
on user needs

13. Wave 3
AI (Per DARPA)
• Machines explore domains,
learning and reasoning as
they go.
• Natural communication
between humans and
machines
• Machines start to reason,
learn from one or two
examples (like humans) – not
the millions of data points
needed today
Design (HCI)
• Culture and context
• Emotions in computing
• Beyond the desktop:
Devices in home and work,
and everywhere else?
• Beyond Keyboard,
Monitor, Mouse control

15. “Invisible” AI
AI is everywhere, and nowhere
• Once an AI technology becomes
ubiquitous, it “disappears” and no
longer is thought of as AI -Henry
Lieberman, 2009
• Spell checking
• Search engines
• Maps and directions
• Voice recognition
• Self-driving cars
• Autonomous robots

16. “Invisible” AI (2)
Neural Nets
• Makes decisions in a
“black box”
• How the decision was
made is hidden
– Based on training data

17. “Numbers Don’t Lie”
• Many times you (and I) do not
know we have a problem
• Your boss probably does not
know this is a problem
• Likely, the creators of these
_ist technologies do not know
it is a problem
• … these are problems

18. Example: “Compas”
• “The mistakes made by COMPAS, however,
affected black and white defendants
differently: Black defendants who did not
recidivate were incorrectly predicted to
reoffend at a rate of 44.9%, nearly twice as
high as their white counterparts at 23.5%... In
other words, COMPAS scores appeared to
favor white defendants over black”
http://advances.sciencemag.org/content/4/1/
eaao5580.full

19. Keep it Like a Secret
Algorithms can be secret,
aka proprietary.
Hard to know how the
decision was made.
“Our Research division will review the data upon
receipt. It is critical to ensure the appropriate
methods, fair comparisons, and conclusions
were made as the article is given wide circulation
online.”
- http://www.equivant.com/blog/official-
response-to-science-advances

20. Google, ACM, and Others
People and AI
“Get practical insights from Google’s People + AI
Research team on how to take a multidisciplinary
and human-centered approach to designing with
machine learning and AI.”
https://design.google/library/ai/
ACM IUI Conference
ACM IUI is where the Human-Computer Interaction
(HCI) community meets the Artificial Intelligence (AI)
community.

21. Jeff Bezos – Day One Fund
Jeff Bezos Announces Launch of $2 Billion Charity
to Help With Education and Homelessness

22. Find your inner Grace Hopper
MUSE Model
MAKE: Humans design and build AI tools.
Ex: Auto-classification (NLP)
USE: Humans use tools augmented by AI.
Ex: Gmail
SENSE: AI senses and learns about users,
acting autonomously.
Ex: Chatbots
ENHANCE: AI co-designs with human
Ex: Website personalization

23. Thank You
Michael Zarro, PhD
@mzarro
www.mikezarro.com
www.linkedin.com/in/mzarro/
Images: https://pixabay.com, unless noted