Whether we like it or not, algorithmic targeted marketing has become a part of the online experience for most users, and it doesn’t look like it’s leaving anytime soon. In an effort to retain users’ time online and increase profits, advertisers create personalized feeds with recommended content, customized advertising, and sponsored stories. In a case study published by MIT Case Studies in Social and Ethical Responsibilities of Computing (SERC), Tanya Kant explores the history, analyzes the current landscape, and considers the ethical consequences of personalized marketing.
Advertisers use computer algorithms to profile users. These algorithms collect the user’s behavior by analyzing clicks, likes, minutes watched, etc. The data is then aggregated and categorized against groups of users to establish what is of personal relevance. These categories are extensive, perhaps even intrusive: gender, age, ethnicity, dietary preferences, facial recognition, political leanings, income, credit status, employment status, relationships — the list goes on.
User data is an invaluable resource for advertisers who not only drive the online economy but also keep the web free. Meta (previously Facebook), the world’s largest data tracker, made $31.43 billion in ad revenue in 2020. However, online platforms possessing user data have led to widespread concern about the ethical responsibility these online platforms have to their users.
Beverley Skeggs, a sociologist at Lancaster University, found that advertisers bid for access to Meta’s data fifty million times per day. This data is then exploited by corporations and institutions such as credit lenders who then target the economically disadvantaged. It was also found that Google had racist categorization inherent in their search engine. Is privacy simply the cost of free access to the internet and these online platforms?
Personalized advertising aims to predict and anticipate users’ wants and needs; however, it is not a perfect matching system in which advertisers provide the best product to the right person. Yet, not everyone sees personalized advertising as a negative. The United Kingdom’s Office of Communications found that 54 percent of online users would prefer to see relevant ads as opposed to non-relevant ads. Yet, most users, especially those who are not as tech-savvy, do not understand the extent that they are tracked. Today, it is near impossible to surf the web without being tracked. Users are tracked from many sources—whether its app location or website cookies—data is always being collected and used.
Since the Facebook-Cambridge Analytica scandal in 2019, there has been a rise in public awareness and scrutiny over user data and personalized advertising. There has been a push to change the discriminatory practices inherent in algorithmic profiling, and how user data is collected and shared. Yet, personalized advertising by its very nature aims to exclude. Tanya Kant recommends a rise in technical literacy so that the public learns about personalized advertising, user data, cookies, etc. All of this raises a question, if an algorithm can learn everything about us, are we nothing more than categories and data?
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