Latanya Sweeney, Racial Discrimination in Online Ad Delivery
Comment by: Margaret Hu
PLSC 2013
Published version available here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2208240
Workshop draft abstract:
Investigating the appearance of online advertisements that imply the existence of an arrest record, this writing chronicles field experiments that measure racial discrimination in ads served by Google AdSense. A specific company, instantcheckmate.com, sells aggregated public information about individuals in the United States and sponsors ads to appear with Google search results for searches of some exact “firstname lastname” queries. A Google search for a person’s name, such as “Trevon Jones”, may yield a personalized ad that may be neutral, such as “Looking for Trevon Jones? Comprehensive Background Report and More…”, or may be suggestive of an arrest record (Suggestive ad), such as “Trevon Jones, Arrested?…” or “Trevon Jones: Truth. Arrests and much more. … “
Field experiments documented in this writing show racial discrimination in ad delivery based on searches of 2200 personal names across two websites. First names, documented by others as being assigned primarily to black babies, such as Tyrone, Darnell, Ebony and Latisha, generated ads suggestive of an arrest 75 percent and 96 percent of the time, and names having a first name documented by others as being assigned at birth primarily to whites, such as Geoffrey, Brett, Kristen and Anne, generated more neutral copy: the word “arrest” appeared zero to 9 percent of the time. A few names did not follow these patterns: Brad, a name predominantly given to white babies, generated a Suggestive ad 62 percent to 65 percent of the time. All ads return results for actual individuals and Suggestive ads appear regardless of whether the subjects have an arrest record in the company’s database. Notwithstanding these findings, the company maintains Google received the same ad copy for groups of last names (not first names), raising questions as to whether Google’s algorithm exposes racial bias in society.