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.

Developing machine learning systems requires multidisciplinary teams working across the ML pipeline. In this workshop, participants are invited to act out a fictional ML design scenario and reflect on how values are embedded and lost in industry practices.

Digital platforms rely heavily on harvesting end-user data to provide personalized content. This master thesis offers a design vision of digital consent practices and disclosure interactions as a process rather than a moment.

We all have unconscious tendencies to categorise and discriminate. These biases are perpetuated by structures of power baked into the data we feed to algorithms. This master thesis offers a collection of queer ambiguous toys, each illustrating a different kind of reflexive practice and designer-AI collaboration.

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.