Annual Showcase

AI imaginaries and their contemporary socio-technical aspects are the focus of this research project. The moral commitments that underlie utopian and dystopian visions with the development of AI are revealed.

A deeper understanding of how health data comes into value is needed to persuasively defend any alternative data governance models. This research project traces the flows of value(s) during the health data creation process. 

Urban explorations are carried out by making and exchanging recipes. This design experiment combines spatial drifting with the recursive practices of recipe-making to facilitate more-than-human commoning.

We cannot decolonize our digital futures without confronting the ghosts from our pasts. This research project extends the temporality of the current speculative model to allow for and actively make space for multiplicity in AI design practices.

Ongoing and experimental speculative readings of Terms of Service (ToS) serve the purpose to reveal what it represents. By doing so, this research project takes a reflective and transformative alternative route.

By expanding design anthropology through the lens of social learning theories and developing novel methods for an ethnographic study of algorithms, this research project contributes to the more-than-human turn in HCI research and to decentralizing the study of algorithmic impact.

By translating ethical guidelines into actual system requirements, this research project focuses on overcoming forms of harmful algorithmic behavior and explores computational strategies for putting ethics into practice in current Machine Learning workflows.

By cooperating with people to investigate what is valuable for them to be translated into data from a non-extractive and more communal perspective, this research project supports the co-creation of data models based on local sets that members collect themselves with participatory and speculative DIY sensors.

Profit-driven services and technologies can be problematic when limiting the ability to interpret reality and perhaps reconcile human and more-than-human worlds. An investigation into how small and urban farmers understand data and how they re-interpret data through practices is carried out.

Using high-level, abstract guidelines in practice can be difficult to apply to specific clinical AI projects. A workshop format has been designed and tested at a clinical AI company to help teams apply guidelines to their project.