Consumer Choice and Data Policies: Sentiment Analysis of User Interviews

“I wish that there was a way for companies to make data policies easier to understand so people aren’t signing blindly. Where’s my data going? I have a master’s degree, and I still don’t understand.”—Gen Z interviewee

Consumers’ preferences, opinions, and thoughts on the design and features of emerging technology are often neglected. Additionally, attempts toward greater explainability and accessibility are focused on the perspective of engineers and data scientists, such as with Mozilla’s AI Guide or the Data Nutrition Project’s Data Labels. Although these industry strides are needed, they often come at the expense of developing accessible tools for the consumer. Additionally, consumer explainability tools focus on a specific product’s underlying feature, such as with Meta’s System Cards, an explainability tool aimed at helping consumers better understand their feed.¹ Apple also released explainability tools for the privacy policies of applications within their App Store. Moreover, exercises in providing choice to consumers largely centered on data broker deletion apps such as Permission Slip or Delete Me. Although these tools are highly valuable, they are not centered around explaining generative AI tools. An accessible, easy-to-read tool that can explain the generative AI tool in use and its data policies is needed to help bridge the ever-widening explainability gap between tool and consumer.

To address this gap and better understand consumers’ needs, qualitative interviews were conducted for three months in 2024 with the goal of collecting information on consumers and their opinions toward generative AI, digital literacy comprehension, understanding of data policies, and attitudes on nutrition labeling. Interviews consisted of a questionnaire, consent form, and an interview. For individuals interested in an interview, a scheduling form was disseminated to prospective interviewees through social media platforms, email, and word of mouth. Interviews ranged in length from 30 to 60 minutes, totaling almost 1,500 minutes of audio data. Participants predominantly lived in the United States (80 percent of respondents), while 14 percent were based in Europe, 3 percent were based in Asia, and the remaining 3 percent resided in Africa. Generations represented included baby boomers, Generation X, millennials, and Generation Z, with a majority of respondents being millennials. Almost all respondents (26) had interacted with a generative AI tool, with only one having no familiarity or past use of generative AI tools. Gaining insight into how individuals across various demographic backgrounds engage with technology—and in particular, generative AI—helped steer the design and information presented on the label.

Overall, respondents reported negative feelings about signing up for and using software tools, including generative AI tools. A sentiment analysis of user data concluded that 33 percent of respondents reported experiencing negative feelings when describing their personal autonomy when signing up for software tools. In addition, users reported lacking significant understanding of the data policies behind the software tools they used. A millennial user referenced feeling that, “I have to suspend my discomfort in order to function in the digital space.” Respondents shared similar feelings on the trade-off between access and paywalls, with one noting, “I’d rather pay for resources to limit the types of personal information I give to companies.” Some respondents within the arts, teaching, and consulting fields noted that access to certain services and tools directly impacted their ability to find work, network, and produce more relevant content. These feelings were compounded when discussing generative AI tools.

Additionally, interviewees often felt a lack of ability to control where their private information was and who had access to it. When discussing comprehension of data policies, respondents noted a lack of understanding, citing incomprehensible legalese. Often, their lack of knowledge due to the legalese resulted in feeling a lack of choice regarding which companies would best protect their private information. Across sectors, including those with post-secondary and terminal degrees, 93 percent noted that they do not understand privacy policies and do not read them. While some blindly accept privacy policies, 33 percent of respondents reported trying to read portions of privacy policies.

“Lack of knowledge due to the legalese resulted in feeling a lack of choice regarding which companies would best protect their private information.”

For artists and teachers, the question of consent and privacy loomed larger. Of those in the artistic and teaching sectors, 100 percent questioned whether the work they created using generative AI tools would be used to further train the model or pass off their work as its own. An actor discussed how the SAG-AFTRA and WGA union strikes affected their ability to work as a stage actor. The actor temporarily switched to voice acting to earn a living and noted during the interview that they wrestled with how their voice could be used without their consent. Another interviewee hypothesized whether users would feel good knowing their work could be used to train AI models, referencing the lack of awareness of how these policies affect user data. Almost all respondents (96 percent) echoed the sentiment of one interviewee who noted that they “don’t feel protected as a consumer.” In addition, interviewees were asked to define personal data. Many respondents highlighted that personal data included the photos they share, their preferences, searches, and other traditionally understood components of personal information.

When asked about habits and attitudes towards labels such as Food and Drug Administration (FDA) nutrition labels, cosmetics labels, and others, 89 percent of respondents revealed that they read labels at a frequency of several times and above. For those respondents, labels helped communicate components of the product, provenance, and potential harms. Some respondents (25 percent) that read labels noted that labeling provides verifiable credentials and a level of trustworthiness that elevates comfort levels. One respondent mentioned that having objective data required by law is not only a powerful advertising function, but also provides much needed information for the consumer to navigate choice. Most respondents wanted to know about the “right to delete” and any potential security concerns. In addition, the ability to scan the most pertinent information was often cited as a must have for a label.

Data collected in these interviews helped create a label prototype using plain language to describe the generative AI tool and skimmable information on the data policies of the generative AI tool. Consumers often noted that traditional nutrition labels are often too dense and use complex language that is hard to understand. The first iteration of the data nutrition label focused on plain language and understandability. Although further testing and consumer feedback is needed, this research provides a starting point for exploring a more robust consumer facing label for generative AI tools.