Social media platforms like Facebook utilize AI algorithms to personalize content based on user data, raising concerns about data privacy and transparency. We introduce the Facebook Data Shield (FDS), a life-sized interactive installation that empowers users to visualize and control the data shared with the platform. We deployed FDS at a public design event, to explore user data-sharing and control preferences. We conducteded an analysis of 81 user interactions, based on data logs and surveys. Our findings reveal a preference for increased data control, particularly concerning online behavior and demographics. We identify five distinct clusters for preferred data-sharing settings, which show limited correlation with demographic information. Finally, we discuss the potential for predicting preferred data-sharing settings through machine learning based on our data, and implications for social media platform design. This study contributes to the ongoing discourse on data governance and user autonomy in an era of AI-driven content curation.

Social media platforms newsfeeds are generated by AI algorithms, which select and order posts based on user data. However, users are often unaware of what data is collected and employed for this aim, neither can they control it. To open up discussions on what data users are willing to feed the newsfeed algorithm with, we created the Facebook Data Shield, a human-size interactive installation where users can see and control what type of data is collected. By pressing buttons, data categories and/or data variables can be (de)activated. An outer rim with lights gives feedback to users about the level of personalization of the resulting newsfeed. We performed a preliminary study to get insights into what data users are willing to share, their preferred level of control, and the effect of such an installation on users' awareness. Based on our findings, we discuss implications for design and future work.