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Potential for Discrimination in Online Targeted Advertising Proceedings of Machine Learning Research 81:1–15, 2018 Conference on Fairness, Accountability, and Transparency Potential for Discrimination in Online Targeted Advertising Till Speicher [email protected] MPI-SWS Muhammad Ali [email protected] MPI-SWS Giridhari Venkatadri [email protected] Northeastern University Filipe Nunes Ribeiro [email protected] UFOP, UFMG George Arvanitakis [email protected] MPI-SWS Fabrício Benevenuto [email protected] UFMG Krishna P. Gummadi [email protected] MPI-SWS Patrick Loiseau [email protected] Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG and MPI-SWS Alan Mislove [email protected] Northeastern University Editors: Sorelle A. Friedler and Christo Wilson Abstract using sensitive attributes. Our findings call for exploring fundamentally new methods Recently, online targeted advertising plat- for mitigating discrimination in online tar- forms like Facebook have been criticized for geted advertising. allowing advertisers to discriminate against Keywords: Discrimination, advertising, users belonging to sensitive groups, i.e., to Facebook exclude users belonging to a certain race or gender from receiving their ads. Such criticisms have led, for instance, Facebook 1. Introduction to disallow the use of attributes such as ethnic affinity from being used by adver- Much recent work has focused on detecting in- tisers when targeting ads related to hous- stances of discrimination in online services rang- ing or employment or financial services. In ing from discriminatory pricing on e-commerce this paper, we show that such measures are and travel sites like Staples (Mikians et al., 2012) far from sufficient and that the problem and Hotels.com (Hannák et al., 2014) to discrimi- of discrimination in targeted advertising is natory prioritization of service requests and offer- much more pernicious. We argue that dis- ings from certain users over others in crowdsourc- crimination measures should be based on ing and social networking sites like TaskRab- the targeted population and not on the at- bit (Hannák et al., 2017). In this paper, we focus tributes used for targeting. We system- atically investigate the different targeting on the potential for discrimination in online ad- methods offered by Facebook for their abil- vertising, which underpins much of the Internet’s ity to enable discriminatory advertising. economy. Specifically, we focus on targeted ad- We show that a malicious advertiser can vertising, where ads are shown only to a subset create highly discriminatory ads without of users that have attributes (features) selected c 2018 T. Speicher et al. Potential for Discrimination in Online Targeted Advertising by the advertiser. Targeted ads stand in con- tom audience2 and look-alike audience3 target- trast to non-targeted ads, such as banner ads on ing that are then subsequently adopted by other websites, that are shown to all users of the sites, online social media and social networking plat- independent of their attributes. forms like Twitter,4 Pinterest,5 LinkedIn,6 and YouTube.7 Thus, many of our findings may also The targeted advertising ecosystem comprises be applicable to these other online ad targeting of (i) advertisers, who decide which users an ad platforms as well. should (not) be shown to; (ii) ad platforms, such as Google and Facebook, that aggregate data Our study here is driven by the following high- about their users and make it available to ad- level question: What are all the different ways vertisers for targeting; and (iii) users of ad plat- in which a Facebook advertiser, out of malice or forms that are consumers of the ads. The po- ignorance, can target users in a discriminatory tential for discrimination in targeted advertising manner (i.e., include or exclude users based on arises from the ability of an advertiser to use their sensitive attributes like race)? the extensive personal (demographic, behavioral, To answer this question, we begin by proposing and interests) data that ad platforms gather an intuitive measure to quantify discrimination about their users to target their ads. An inten- in targeted ads. We then systematically investi- tionally malicious—or unintentionally ignorant— gate three different targeting methods (attribute- advertiser could leverage such data to preferen- based targeting, PII-based targeting, and look- tially target (i.e., include or exclude from tar- alike audience targeting) offered by Facebook for geting) users belonging to certain sensitive social their ability to enable discriminatory advertising. groups (e.g., minority race, religion, or sexual ori- At a high-level, we find that all three methods en- entation). able advertisers to run highly discriminatory ads. Worse, we show that the existing solution ap- Recently, the Facebook ad platform was the proaches of banning the use of certain attributes target of intense media scrutiny (Angwin and like “ethnic affinity” in targeting is not only in- Parris Jr., 2016) and a civil rights lawsuit for adequate, but does not even apply in two out of allowing advertisers to target ads with an at- the three ad targeting methods. tribute named “ethnic affinity.” After clarifying While our findings primarily serve to demon- that a user’s “ethnic affinity” does not represent strate the perniciousness of the problem of dis- the user’s ethnicity, but rather represents how criminatory advertising in today’s ad platforms, interested the user is in content related to dif- it also lays the foundations for solving (i.e., de- ferent ethnic communities, Facebook agreed to tecting and mitigating) ad discrimination. not allow ads related to housing, employment, and financial services be targeted using the at- tribute (Facebook, 2017) and renamed it to “mul- 2. Quantifying Ad Discrimination ticultural affinity.”1 In this paper, we conduct a systematic study Next, we begin by outlining different ad targeting of the potential for discriminatory advertising on methods offered by Facebook. We then discuss the Facebook advertisement platform. We focus the current approach to determining whether an on Facebook because it is one of the largest online ad is discriminatory and argue why it is inade- advertising platforms in terms of number of users 2. https://www.facebook.com/business/help/ reached by ads, the number of advertisers, and 170456843145568 the amount of personal data gathered about the 3. https://www.facebook.com/business/help/ users that is made available to advertisers. Fur- 164749007013531 thermore, Facebook is an innovator in introduc- 4. https://business.twitter.com/en/targeting/ tailored-audiences.html ing new methods for targeting users, such as cus- 5. https://business.pinterest.com/en/blog/new- targeting-tools-make-pinterest-ads-even-more- effective 6. https://business.linkedin.com/marketing- 1. Unfortunately, Facebook was found half a year later solutions/ad-targeting/matched-audiences to still accept discriminatory ads, despite the fixes it 7. https://support.google.com/youtube/answer/ claims were put in place (Angwin et al., 2017a). 2454017 2 Potential for Discrimination in Online Targeted Advertising quate. Finally, we propose a new and intuitive discriminatory is based on the process used to approach to quantify discrimination. target the ads. Any ads placed using the right process would be non-discriminatory (by defini- 2.1. Methods for targeted ads tion), while those using a wrong process would be declared discriminatory (by definition). Ex- Facebook gathers and infers several hundreds of isting approaches, such as those that determine attributes for all of its users, covering their demo- whether an ad is discriminatory based on the use 8 graphical, behavioral, and interest features (An- of sensitive attributes (e.g., “ethnic affinity”) in dreou et al., 2018). Some of those attributes, such targeting, fall under this category. As we show in as gender or race, are considered sensitive mean- this paper, attempting to quantify discrimination ing targeting (i.e., including or excluding) peo- based on the process (means or methods) of tar- ple based on those attributes is restricted by law geting is quite difficult when there exist multiple for certain types of advertisements (e.g., those different processes for targeting users. Instead, announcing access to housing or employment or in this work, we advocate for a third approach. financial services (Barocas and Selbst, 2016)). 3. Based on targeted audience (outcomes): Facebook allows advertisers to select their tar- We propose to quantify discrimination based on get audience in three ways: the outcomes of the ad targeting process, i.e., the 1. Attribute-based targeting: Advertisers can audience selected for targeting. Put differently, select audiences that have (or do not have) we do not take into account how users are be- a certain attribute (or a combination of at- ing targeted but only who they are. Outcome- tributes), e.g., select users who are “men”, based approaches to quantifying discrimination “aged 35”, and are interested in “tennis.” have the advantage that they can be generally applied to all scenarios, independently of the em- 2. PII-based (custom audience) targeting: Ad- ployed method of targeting. We discuss one such vertisers can directly specify who should be method in the next section. targeted by providing a list of personally identifiable information (PII) such as phone 2.3. Outcome-based discrimination numbers or email addresses. To formalize our discrimination measure, we will 3. Look-alike audience targeting: Advertisers assume that an ad platform like Facebook keeps can ask Facebook to target users who are track of a database D = (u ) of user- similar to (i.e., “look like”) their existing set i i=1;:::;n records ui where each user is represented by a of customers, specified using their PII. m vector of boolean attributes, i.e., ui 2 B . We 2.2. Quantification approaches denote the sensitive attribute (e.g., race or gen- der) that we are interested in a particular situa- Next, we discuss three basic approaches to quan- tion by s 2 f1; : : : ; mg and its value for a user u tifying discrimination and their trade-offs.
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