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.

Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services. However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix and Match Machine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers' ideation processes. The tokens represent data types and ML capabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potential impact of a hybrid toolkit for ML on design education and practice.

When designing Machine Learning (ML) enabled solutions, designers often need to simulate ML behavior through the Wizard of Oz (WoZ) approach to test the user experience before the ML model is available. Although reproducing ML errors is essential for having a good representation, they are rarely considered. We introduce Wizard of Errors (WoE), a tool for conducting WoZ studies on ML-enabled solutions that allows simulating ML errors during user experience assessment. We explored how this system can be used to simulate the behavior of a computer vision model. We tested WoE with design students to determine the importance of considering ML errors in design, the relevance of using descriptive error types instead of confusion matrix, and the suitability of manual error control in WoZ studies. Our work identifies several challenges, which prevent realistic error representation by designers in such studies. We discuss the implications of these findings for design.