2023-10-30, Savic, Selena, Martins, Yann Patrick

This article presents an approach to practicing ethics when working with large datasets and designing data representations. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that machine learning models can be navigated in a multi-narrative manner when access to training data is well articulated and understood. We programmed and used web-based interfaces to sort, organize, and explore a community-run digital archive of radio signals. An additional perspective on the question of working with datasets is offered from the experience of teaching image synthesis with freely accessible online tools. We hold that the main challenge to social transformations related to digital technologies comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. To counter both the unfounded narratives of techno-optimismand the universalizing critique of technology, we discuss an approachto data and networks that enables a situated critique of datafication and correlationism from within.

2022-06-09, Savic, Selena, Martins, Yann Patrick

Whether we are discussing measures in order to ‘flatten the curve’ in the ongoing pandemic, or what to wear in face of the most recent weather forecast, we make arguments based on patterns and trends observed in data. What makes these patterns observable? Making arguments with data requires critical engagement with datasets, as well as computational processes to gather data, to organize and model their relationships. This article presents an approach to practicing ethics when working with large datasets and designing data representations. The arguments we make are based on the development and use of a computational instrument, and working with digital archives. We programmed and used web-based interfaces to sort, organize and explore a community-ran archive of radio signals. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that one can navigate machine learning models in a multi-narrative manner, and that knowledge of radio signals or any other technical artefact transgresses domains. We propose visual explorations of complex data structures that enable storytelling and an understanding of datasets that resists extraction of discrete identities from the data. We hold that the main challenge to sovereignty comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. Countering both the unbased narratives of techno-optimism, and the universalizing critique of technology, we discuss an approach to data and networks that enables a situated critique of datafication and correlationism from within.

2023-02, Savic, Selena, Martins, Yann Patrick, Herlo. Bianca, Irrgang, Daniel

Whether we are discussing measures in order to "flatten the curve" in a pandemic or what to wear given the most recent weather forecast, we base arguments on patterns observed in data. This article presents an approach to practicing ethics when working with large datasets and designing data representations. We programmed and used web-based interfaces to sort, organize, and explore a community-run archive of radio signals. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that one can navigate machine learning models in a multi-narrative manner. We hold that the main challenge to sovereignty comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. Countering both narratives of techno-optimism and the universalizing critique of technology, we discuss an approach to data and networks that enables a situated critique of datafication and correlationism from within.

2021, Savic, Selena, Martins, Yann Patrick, Botta, Massimo, Junginger, Sabine

This paper will present learnings from a 4-year SNSF-funded research project (2018- 2021), exploring commoning initiatives through regular exchange with three housing cooperatives from Switzerland. In close cooperation with them, we developed four agent- based models as visions for dividing up work needed to care for common spaces and resources in a sustainable way. We affirm computational modelling as a design praxis that can address commoning as a world-making activity, and explore mechanisms that would challenge or restore the stability of community life simulated in this way. Our models are not to be understood as prediction-oriented systems, but rather as a process of designing thinking tools, or toys by which we are creating ways of being. What kinds of controls can prevent extraction of resources from the community? What personal strategies bring more harmony to the group and how much does individual behaviour affect it? We address these questions and propose some preliminary conclusions about the entanglements of labour with value extraction in commoning activities that are best addressed through stories.

2023, Bedö, Viktor, Martins, Yann Patrick, Güngör, Ozan

The IoT Cards for Predictive Food Rescue explores specificities of commoning- and care-based data-driven infrastructure through the lens of prototyping cards. The cards were developed by an experimental design project investigating the predictive distribution of rescued food and the inherent friction between heterogenous situated cooking habits and city-wide infrastructure. The cards present themselves as a subversive extension pack for the IoT Service Kit, a third party open-source prototyping toolset for IoT service design.

2020, Savic, Selena, Bedö, Viktor, Büsse, Michaela, Martins, Yann Patrick, Miyazaki, Shintaro

This article explores the use of agent-based modelling as a critical and playful form of engagement with cooperative housing organizations. Because of its inherent complexities vis-à-vis decision-making, commoning is a well-suited field of study to explore the potential of humanities-driven experimental design (media) research to provoke critical reflection, problem-finding and productive complication. By introducing two different agent-based models, the interdisciplinary research team discusses their experience with setting up parameters for modelling, their implications, and the possibilities and limits of employing modelling techniques as a basis for decision-making. While it shows that modelling can be helpful in detecting long-term results of decisions or testing out effects of unlikely yet challenging events, modelling might act as a discursive practice uncovering hidden assumptions inherent in the model setup and generating an increase of scientific uncertainty. The project “ThinkingToys for Commoning” thus argues for a critical modelling practice and culture, in which models act as toys for probing alternative modes of living together and explor- ing the constructedness of methods. In countering late forms of capitalism, the resulting situated and critical practice provides avenues for enabling more self-determined forms of governance.