Daryl

June 2024

Annotated Bibliography: Consumer Data Privacy

Research Question: Is consumer data safe in today’s digital age?

Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2021). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619

“This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined” (et. al. 2021). This review provides a breakdown and semi-brief explanation of the evolution, role and function of artificial intelligence. It’s useful for me because it gives explanations and definitions of terms related to my research paper. I will attempt to use it as reference to introduce the overall idea of the systems in place being used to collect and, analyze and use available consumer data.

Endo, S. K. (2021). Ad Tech & the future of Legal Ethics. Social Science Research Network. Alabama Law Review. 2021, Vol. 73 Issue 1, p107-157. 51p <span class="MsoHyperlink">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3937773</span>

              “Privacy scholars have extensively studied online behavioral advertising, which uses Big Data to target individuals based on their characteristics and behaviors. This literature identifies several new risks presented by online behavioral advertising and theorizes about how consumer protection law should respond” (Endo, 2021). The article seems to be focus more toward lawyers’ use of AI and advertising to target clients specifically. This is actually a perfect example of how companies, customers and ethics all tie in to each other. In terms of my paper, I can use it to demonstrate the tension that is created and constantly being explored, sometimes dancing over the line of ethical and unethical data use.

Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022). Explaining how algorithms work reduces consumers’ concerns regarding the collection of personal data and promotes AI technology adoption. Psychology & Marketing, 39(10), 1888–1901. <span class="MsoHyperlink">https://doi.org/10.1002/mar.21705</span>

“Consumers' concerns about how companies gather and use their personal data can impede the widespread adoption of artificial intelligence (AI) technologies. This study demonstrates that mechanistic explanations of AI algorithms can inhibit such data collection concerns” (et. al. 2022).

The problem most consumers have is that they usually don’t know why their data is being collected and what it’s being used for, understandably. The article explains that studies show when consumers are properly educated on how their data is collected, they are more receptive and willing to release their data. Transparency is a key aspect of ethical consumer data use and I will use this in my paper to show evidence and results of ethical data use.

Carter M. The Optimal Opt-In Option: Protecting Vulnerable Consumers in the Expanding Privacy Landscape. Columbia Law Review. 2024; 124(2):431-458. Accessed May 31, 2024. <span class="MsoHyperlink">https://search-ebscohost-com.ezproxy.umgc.edu/login.aspx?direct=true&db=bsu&AN=177093318&site=eds-live&scope=site</span>

This journey article explores how users’ data is vulnerable and therefore can be used to exploit and discriminate the user. It also cover the general landscape of data privacy and the origin and purpose of Privacy Acts (p. 440). A prime example of unethical data use is racial discrimination. I will use this article to show the evidence and ways entities are unethically collecting and using user data. “The information and data that are collected on people can be twisted and used in discriminatory ways . . . Businesses find it helpful when these algorithms categorize and recognize patterns in the data that they can use. Although the concepts of information and patterns on their own give the impression of impartiality, bias and racism thrive off Big Data analysts sharing and selling data . . . law enforcement buys these data to build massive and discriminatory police surveillance networks” (Carter, 2024).