nation—advertiser bias in placing ads or society bias in selecting ads.

Discrimination, however, is at the heart of online advertising. Differential delivery is the very idea behind it. For example, if young women with children tend to purchase baby products and retired men with bass boats tend to purchase fishing supplies, and you know the viewer is one of these two types, then it is more efficient to offer ads for baby products to the young mother and fishing rods to the fisherman, not the other way around.

On the other hand, not all discrimination is desirable. Societies have identified groups of people to protect from specific forms of discrimination. Delivering ads suggestive of arrest much more often for searches of black-identifying names than for white-identifying names is an example of unwanted discrimination, according to American social and legal norms. This is especially true because the ads appear regardless of whether actual arrest records exist for the names in the company’s database.

The good news is that we can use the mechanics and legal criteria described earlier to build technology that distin-guishes between desirable and undesirable discrimination in ad delivery. Here I detail the four key components:

1. Identifying Affected Groups. A set of predicates can be defined to identify members of protected and comparison groups. Given an ad’s search string and text, a predicate returns true if the ad can impact the group that is the subject of the predicate and returns false otherwise. Statistics of baby names can identify first names for constructing race and gender groups and last names for grouping some ethnicities. Special word lists or functions that report degree of membership may be helpful for other comparisons.

In this study, ads appeared on searches of full names for real people, and first names assigned to more black or white babies formed groups for testing. These black and white predicates evaluate to true or false based on the first name of the search string.

2. Specifying the Scope of Ads to Assess. The focus should be on those ads capable of impacting a protected group in a form of discrimination prohibited by law or social norm. Protec-

Discrimination
is at the heart of
online advertising.
Differential
delivery is
the very idea
behind it.

tion typically concerns the ability to give or withhold benefits, facilities, services, employment, or opportunities. Instead of lumping all ads together, it is better to use search strings, ad texts, products, or URLs that display with ads to decide which ads to assess.

This study assessed search strings of first and last names of real people, ads for public records, and ads having a specific display URL ( instantcheckmate.com), the latter being the most informative because the adverse ads all had the same display URL.

Of course, the audience for the ads is not necessarily the people who are the subject of the ads. In this study, the audience is a person inquiring about the person whose name is the subject of the ad. This distinction is important when thinking about the identity of groups that might be impacted by an ad. Group membership is based on the ad’s search string and text. The audience may resonate more with a distinctly positive or negative characterization of the group.

3. Determining Ad Sentiment. Originally associated with summarizing product and movie reviews, sentiment analysis is an area of computer science that uses natural-language processing and text analytics to determine the overall attitude of a writing. 13 Sentiment analysis can measure whether an ad’s search string and accompanying text has positive, negative, or neutral sentiment. A literature search does not find any prior application to online ads, but a lot of research has been done assessing sentiment in social media (sen- timent140.com, for example, reports the sentiment of tweets, which like advertisements have limited words).

In this study, ads containing the word arrest or criminal were classified as having negative sentiment; ads without those words were classified as neutral.

4. Testing for Adverse Impact. Consider a table where columns are comparative groups, rows are sentiment, and values are the number of ad impressions (the number of times an ad appears, though the ad is not necessarily clicked). Ignore neutral ads. Comparing the percentage of ads having the same positive or negative sentiment across groups reveals the degree to which one group may be impacted more or less by the ad’s sentiment.