table 2. Negative and neutral sentiments of black and white groups.

negative
neutral
Positive
totals
Black
291 60%
197 40%

488

White 308 48% 330 52%

638

 

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.

acknowledgments

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.

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Jeffrey Heer, Ben Shneiderman

http://queue.acm.org/detail.cfm?id=2146416

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Latanya Sweeney ( [e-mail]) 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.