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Column: Americans hate when companies infer their personal info. Marketers keep doing it anyway.

By Rena Coen, Slate
Published: December 27, 2017 Updated: December 27, 2017 at 02:10 PM
This photo provided by Universal Pictures shows, Aldis Hodge, from left, as MC Ren, Neil Brown, Jr. as DJ Yella, Jason Mitchell as Eazy-E, OíShea Jackson, Jr. as Ice Cube and Corey Hawkins as Dr. Dre, in the film, ìStraight Outta Compton." The movie releases in U.S. theaters on Friday, Aug. 14, 2015. (Jaimie Trueblood/Universal Pictures via AP)

Last year, Facebook presented users with different ads for the film Straight Outta Compton based on their "ethnic affinity." The targeted users had not checked a box identifying themselves as black. Instead, the social marketers algorithms had guessed their "ethnic affinity" based on posts they had clicked on and liked in the past.

Its happened before. In what has become a go-to cautionary tale, Target infamously used inference to send a teenager advertisements for baby items before she had even told her family she was pregnant; the companys algorithms had predicted her maternal state based on her purchase data and demographic profile. In another case, ProPublica busted Princeton Review for charging different prices for its SAT prep courses based on ZIP code, which meant that Asian families were nearly twice as likely to be quoted a higher price. Home Depot, Staples and other companies similarly have been caught varying prices by location. While likely unintentional in these cases, the outcomes highlight that location information can serve as an effective proxy for race or income.

If a company asks you online to provide detailed information about yourself, you might be reluctant to share it. So digital marketers have found a workaround: They vacuum up troves of data about internet users to improve their use of inference, the practice of using available information like behavioral, mobile, and publicly available data to make informed guesses about consumers. By deducing your race, income, gender, interests and more, companies can personalize search results, ads, pricing and other content.

Marketers love personalized ads because they perform well about three times better than standard ads, according to research by web marketing firm Jivox. However, the use of inference has generated concerns about discrimination. Following the Straight Outta Compton uproar, ProPublica reported that it was able to place a housing-related Facebook ad that excluded those of black, Asian and Hispanic ethnic affinity. Facebook claimed to crack down after this feature was found to be in violation of the Fair Housing Act, but just weeks ago, ProPublica confirmed the company has continued to allow discriminatory ads to go through.

For example, while posing as a housing rental agency, ProPublica purchased a Facebook ad with settings to exclude potential renters "interested in Islam, Sunni Islam and Shia Islam." The ad was approved in 22 minutes. Of course, Facebook users do not explicitly report their race or ethnic affinity, and while there is a profile field for religion, a lot of users dont give this information. Facebooks algorithms fill in the blanks through probabilistic models or inference.

Yet many Americans arent aware that social networks and advertisers are offering them personalized content based on inference algorithms. When they learn about it, they often dislike the idea. In 2016, my colleagues Emily Paul, Pavel Venegas and I conducted a research study in partnership with the Center for Democracy and Technology, focused on understanding attitudes about inference and personalization. Out of 748 U.S. internet users surveyed, 58 percent had never or rarely thought about ads targeting them based on their inferred race, and 65 percent found the idea to be unacceptable or somewhat unacceptable.

Respondents particularly disliked the use of race for personalizing the prices of products, with only 8 percent saying that using their inferred race would be somewhat acceptable or acceptable. Across personalization contexts, many participants connected the use of race to broader discrimination and societal implications: "Didnt we determine racial profiling was inappropriate?" one survey participant asked. "Why is it okay for a corporation to behave in this manner?"

Most web users surveyed dont like marketing based on their household income level, either. Some respondents had emotional responses to the idea that their income might influence the marketing they see, using words like "rude," "scary" and "creepy."

Our study suggests that the industry needs new standards to better inform and empower people to protect their personal information. This requires not only allowing users control over what data are collected about them but also what is inferred about them, how those inferences are used and by whom. Its currently rare for companies to give users a window into this inference-based personalization online, but Google offers a step in the right direction, giving users the ability to view (and, if theyre incorrect, modify) age, gender, and interest inferences, and the option to turn off ad personalization completely.

Industry would have us believe that personalization is good for everyone, seamlessly connecting buyers and sellers, regardless of what drives those connections. We can expect its use to continue.

For example, recent research demonstrated that algorithms can be used to scan Instagram photos for markers of mental health. While it may be tempting to use such information without our knowledge for marketing or pricing (insurance or otherwise), our research suggests thats not the kind of personalization Americans want.

Rena Coen is a policy analyst at Berkeley Law and an Internet Law and Policy Foundry fellow.


2017 Slate