discrimination, digitial markets, and design
TRANSCRIPT
Discrimination,
Digital Markets, and
Design
R e i m a g i n i n g t h e r o l e o f d e s i g n i n t h e f i g h t
a g a i n s t d i s c r i m i n a t i o n
• Discrimination is occuring
in digital marketplaces
The Argument + Background
• Intentional design can
combat discrimination
• We can do better
Distinction
UI Design Process
Design
Algorithmic
Design
Title Name
Sweeney: traditionally black names in Google were more likely to produce adds offering to investigate possible arrest records
• Examples: eBay,
Amazon, Priceline
• Relative anonymity
• Users can negotiate
without identifying
information
• Reviews began to
proliferate
1st
Evolution
2nd
• Examples: Uber,
Airbnb, Etsy
• Zero anonymity:
users create profiles
with highly granular
data
Growth
• Uber: 2 Billion rides as of July 2016
(Wired)
• Didi: 1.43 Billion rides in 2015 alone
(Wired)
• Airbnb: 3000 listings in 2009, 2.3
million in 2016
THREATS
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• “We bet our whole company on the
hope that, with the right design, people
would be willing to overcome the
stranger danger bias.” – Joe Gebbia
Trust
Forthcoming: Ahuja
and Lyons
• Guests with traditionally
black names in the U.S.
are 16% less likely to be
accepted on Airbnb
January 2016: Edelman,
Luca, and Svirsky
• Users start complaining on
social media about
experiences of
discrimination + uptick in
news coverage
#AirbnbWhileBlack
October 2016: Ge, Knittel,
MacKenzie, and Zoepf
• Male same-sex couples in
Dublin, Ireland are 13%
less likely to be accepted
on Airbnb
• Seattle: longer wait times
(up to 35% longer)
• 2x chance of cancellation
for traditionally black
names
Edelman, Luca, and Svirsky
Design
Treatment
Results
Implications
Fake profiles are created with consistent
characteristics, randomely selected hosts to recieve
message from guest with a traditionally black name
Field experiment involving 6,400 Airbnb listings
across 5 American cities
Guests with traditionally black names were
16% less likely to get accepted. Robust to
a variety of characteristics
Compared to an online hotel
website which has a
discrimination rate of 0%
Ahuja & Lyons
Design
Treatment
Results
Implications
Fake profiles are created with consistent
characteristics, randomely selected hosts to recieve
message from guest that self-identified being in a
same-sex relationship
Field experiment involving 800 Airbnb listings in
Dublin, Ireland
Male guests that self-identified as being in
a same-sex relationship were 13% less
likely to be accepted
Compared to an online hotel
website which has a
discrimination rate of 0%
What’s the
problem?
Picture: pre-
transaction, guest
pictures can lead to
rejections based on
implicit or explicity bias
Name: pre-
transaction, guest
names can lead to
rejections based on
implicit or explicity bias
Optionality: pre-
transaction, host
decision making
leaves the platform
vulnerable to
discrimination
TEXT
IMAGE IMAGE
HOME
SLIDER
FOOTER
Instant Book!
Airbnb doesn’t release
statistics about the # of
hosts who have
“Instant Book”
Globally• Financial incentives for instant
book option
• Behavioral nudges to set instant
book (default option, etc.)
• Remove optionality entirely
Significant Opportunity
Percentage of Airbnb
hosts that have the
“Instant Book” feature
in Ahuja & Lyons
Dublin Sample
Percentage of Airbnb
hosts that have the
“Instant Book” in the
Dublin market
Dublin Airbnb Market
16%
?
14%
INFORMATION
• Withold potentially
sensitive user
information
• Airbnb vs. Homeaway
AUTOMATION COMMUNICATION
Key Principles
• Instant booking, timing
of personal information
in transactions
• Uber vs. Lyft
• Smarter prompting and
nudges could increase
the salience of
potential bias
Platforms exist within a larger social context!
Fixing Discrimination in Online Marketplaces: Ray Fisman and Michael Luca
• Discrimination is occuring
in digital marketplaces
Convinced?
• Intentional design can
combat discrimination
• We can do better
Thank You about.me/rishiahuja
facebook.com/rahuja360
@RN_Ahuja
Linkedin.com/in/rishiahuja