Massive data analytics industries have allowed marketers to access previously sparse and inaccessible personal information, leveraging and optimizing it through the use of savvy algorithms.
Predatory advertising depends, in large part, on the deliberate exploitation of individuals based on specific traits, life circumstances, or membership within certain groups.
These can be incredibly hard to classify and regulate as some claims may be true at face-value, but rely on either tactical omissions of information or the contextual circumstances of the individual to draw inferences that may be false.
As reliance on digital platforms has become almost necessary for participation in modern life, individuals have been asked to relinquish large amounts of personal information, either through direct submission or by inference from user engagement.
[6] Though many of these are relatively benign or even positive, often being utilized to tailor personalized user-experiences, the availability of such data to unethical marketers has inflamed problems of predatory advertising.
Much of the information originates from discrete sources, including social media engagement, loyalty programs and purchasing history from online retailers, web browser queries, government records, and mobile application usage and preferences.
A 2013 report by the Federal Trade Commission found that data brokerage companies compiled individuals into groups with labels such as: "Zero Mobility," "Credit Crunched: City Families," "Rural and Barely Making It," "Enduring Hardships," and "Tough Start: Young Single Parents.
One consequence of this is that traditionally protected information, such as health outcomes, race, or private financial histories, can be inferred with greater certainty without ever collecting data on the specific item in question.
"[9] While this process optimizes the ability to provide users with an individualized experience, it alleviates much of the culpability traditionally placed on ad-revenue dependent platforms to monitor their ad placements.
Furthermore, when the algorithms are built using grouping labels such as those listed in the previous section (i.e. "Burdened by Debt: Singles"), advertisers looking to target and exploit specific characteristics can easily reach the most vulnerable populations.
[15] Other studies have shown that for-profit institutions attract a disproportionate number of low-income minorities through advertisement practices that capitalize on dampened social mobility through the promise of career placement.
Some researchers have called this phenomenon "predatory inclusion," whereby the necessity for fringe institutions providing "alternative" services is only made possible through larger, structural socioeconomic dynamics.
[18] The use of data-driven micro-targeting has allowed politicians to tailor messages to specific individuals, speaking directly to the preferences, concerns, interests, and fears that they may have displayed through their online activity.
[9] While these practices may be largely benign, by allowing politicians to increase engagement by using individual names or campaigning on individually-relevant issues, critics have noted some disastrous effects on democratic processes.
One of the most notable examples is the Cambridge Analytica scandal, wherein the consulting firm was found to have utilized large amounts of personal data to create highly-inflammatory targeted material, having purported impact on numerous international elections.
Another common tactic is the structuring of advergames so that the attainment of the product is the desired goal (as in, acquiring the candy bar or equivalent awards the player with a point value or prize).
[23] In the United States, many of the regulatory efforts put forth in response to predatory advertising practices, especially those involving the usage of personal data, have been spearheaded by the Federal Trade Commission.