table 2. Negative and neutral sentiments
of black and white groups.
A chi-square test can determine statistical significance, and the adverse
impact test used by the EEOC and the
U.S. Department of Labor can alert
whether in some circumstances legal
risks may result.
In this study the groups are black
and white, and the sentiments are negative and neutral. Table 2 shows a summary chart. Of the 488 ads that appeared
for the black group, 291 (or 60%) had
negative sentiment. Of the 638 ads displayed for the white group, 308 (or 48%)
had negative sentiment. The difference
is statistically significant (X2( 1)= 14. 32, p
< 0.001) and has an adverse impact measure of (40/52), or 77%.
An easy way of incorporating this
analysis into an ad exchange is to decide which bias test is critical (for example, statistical significance or adverse impact test) and then factor the
test result into the quality score for the
ad at auction. For example, if we were to
modify the ad exchange not to display
any ad having an adverse impact score
of less than 80, which is the EEOC standard, then arrest ads for blacks would
sometimes appear, but would not be
overly disproportionate to whites, regardless of advertiser or click bias.
Though this study served as an example throughout, the approach generalizes to many other forms of discrimination and combats other ways ad
exchanges may foster discrimination.
Suppose female names tend to get
neutral ads such as “Buy now,” while
male names tend to get positive ads
such as “Buy now. 50% off!” Or suppose black names tend to get neutral
ads such as “Looking for Ebony Jones,”
while white names tend to get positive
ads such as “Meredith Jones. Fantastic!”
Then the same analysis would suppress
some occurrences of the positive ads so
as not to foster a discriminatory effect.
This approach does not stop the
appearance of negative ads for a store
placed by a disgruntled customer or
ads placed by competitors on brand
names of the competition, unless these
are deemed to be protected groups.
The author thanks Ben Edelman,
Claudine Gay, Gary King, Annie Lewis,
and weekly Topics in Privacy participants (David Abrams, Micah Altman,
Merce Crosas, Bob Gelman, Harry
Lewis, Joe Pato, and Salil Vadhan) for
discussions; Adam Tanner for first
suspecting a pattern; Diane Lopez and
Matthew Fox in Harvard’s Office of
the General Counsel for making publication possible in the face of legal
threats; and Sean Hooley for editorial
suggestions. Data from this study is
available at foreverdata.org and the
IQSS Dataverse Network. Supported in
part by NSF grant CNS-1237235 and a
gift from Google, Inc.
Modeling People and Places with Internet
David Crandall, Noah Snavely
Interactive Dynamics for Visual Analysis
Jeffrey Heer, Ben Shneiderman
James L. Crowley
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Latanya Sweeney ( email@example.com) is professor
of government and technology in residence at harvard
university. she creates and uses technology to assess
and solve societal, political, and governance problems and
teaches others how to do the same. she is also founder
and director of the Data Privacy lab at harvard.