The rapid adoption and use of generative AI tools, like ChatGPT, has led to an exponential increase in its environmental impact. However, most users are unaware of this impact. To investigate and raise public awareness of the impact, we designed ecoAIware, a set of five tangible participatory data physicalizations (PDPs) highlighting different environmental impacts of genAI. ecoAIware was deployed at a library and an academic conference, where visitors could interact with the PDPs by contributing tokens representing their awareness of the different impacts. In this pictorial, we present our findings on current awareness levels of visitors, their engagement with ecoAIware and how it contributed to raising their awareness. This pictorial contributes to (i) the discourse within HCI communities around the environmental impact of genAI and (ii) by showcasing how creative methods such as PDPs can be used to engage users with critical topics and collect data in the process.

In the era of globalization, the intertwining of cultures poses both opportunities and challenges. While cultural hybridization fosters intercultural exchange, it risks eroding cultural identities. This paper introduces WonderMap, initially centered on the Netherlands but with broader applications. This interactive map bridges local and international communities, empowering local voices, nurturing cross-cultural understanding, and fostering a global community. WonderMap integrates tangible maps, Near Field Communication (NFC) technology, and digital interfaces, allowing users to share narratives and foster genuine connections. Currently limited to specific cities, WonderMap envisions future upgrades, incorporating advanced technologies for precise location identification and immersive experiences. By balancing cultural diversity and global interconnectedness, WonderMap seeks to celebrate diverse cultures and promote mutual understanding in our interconnected world.

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 & 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.

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.