figure 5. Senator Claire mcCaskill’s
campaign ad appeared next to an ad
using the word “arrest.”
figure 6. an assortment of ads appearing
for Latisha Smith.
(a)
(b)
(c)
(d)
These findings reject the hypothesis
that no difference exists in the delivery
of ads suggestive of an arrest record
based on searches of racially associated names.
additional observations
The people behind the names used
in this study are diverse. Political fig-
ures included Maryland State Repre-
sentatives Aisha Braveboy (arrest ad)
and Jay Jacobs (neutral ad); Jill Biden
(neutral ad), wife of U.S. Vice Presi-
dent Joe Biden; and Claire McCaskill,
whose campaign ad for the U.S. Sen-
ate in Missouri appeared alongside an
Instant Checkmate ad using the word
arrest (Figure 5). Names mined from
academic websites included graduate
students, staff, and accomplished aca-
demics, such as Amy Gutmann, presi-
dent of the University of Pennsylvania.
Dustin Hoffman (arrest ad) was among
names of celebrities used. A smorgas-
bord of athletes appeared, from local
to national fame (assorted neutral and
arrest ads). The youngest person whose
name was used in the study was a miss-
ing 11-year-old black girl.
more about the Problem
Why is this discrimination occurring?
Is Instant Checkmate, Google, or so-
ciety to blame? We do not yet know.
Google understands that an advertiser
may not know which ad copy will work
best, so the advertiser may provide
multiple templates for the same search
string, and the “Google algorithm”
learns over time which ad text gets the
most clicks from viewers. It does this
by assigning weights (or probabilities)
based on the click history of each ad. At
first, all possible ad texts are weighted
the same and are equally likely to pro-
duce a click. Over time, as people tend
to click one ad copy over others, the
weights change, so the ad text getting
the most clicks eventually displays
more frequently.
technical Solutions
How can technology solve this problem? One answer is to change the
quality scores of ads to discount for
unwanted bias. The idea is to measure real-time bias in an ad’s delivery
and then adjust the weight of the ad
accordingly at auction. The general
term for Google’s technology is ad exchange. This approach generalizes to
other ad exchanges (not just Google’s);
integrates seamlessly into the way ad
exchanges operate, allowing minimal
modifications to harmonize ad deliveries with societal norms; and, works
regardless of the cause of the discrimi-
