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Call for Papers: Breaking Models, Data Governance and New Metrics of Knowledge in the Time of the Pandemic

The COVID pandemic has made us all familiar with graphs that predict the degree of collective contagion and, on a daily basis, warn the population about the risk of infection. Such a pervasive computational infrastructure for tracking, measuring and forecasting the behaviour of the social body is unprecedented in the history of healthcare and biopolitics, finding a parallel only in the “vast machine” (Edwards 2010) of global weather forecasting and climate science. The infrastructure for monitoring the pandemic was not created ex nihilo, in fact, but built upon existing digital platforms that already organise most of the social relations of the present (and that further consolidated monopoly positions thanks to the emergency). 
The interdisciplinary workshop BREAKING MODELS aims to study the influence of these vast computational platforms, predictive models and metrics not only on the knowledge of the pandemic, but also on society at large, that is on labour, education and scientific research. The workshop will analyse the epistemic frictions of three technical components of the COVID pandemic’s governance: platforms, models and metrics. 

  • Platforms. The pandemic has been mapped through the same tools, namely digital media, that are also used for communication (messaging apps), commerce (online shopping platforms), education (video conferencing) and work (home office software, gig platforms). Digital networks are interchangeably employed for administrative and corporate purposes, for marketing and education as much as for health governance and data extraction. The workshop will address the function creep of these platforms: the fact that the means of communication and production have increasingly become the same as those of control and surveillance.
  • Models. The outbreak of the pandemic has challenged existing predictive models, proving their fragility and engendering an interest in new modelling techniques, such as machine learning. To what extent are these AI models and their correlations robust enough to forecast rare events and anomalies? The workshop will address the mediation of  statistical models in the representation of reality and, specifically in machine learning, the replacement of the traditional scientific episteme of causation by one of correlation.
  • Metrics. The relationship between models, data and reality cannot be understood without also considering the constitution of metrics: the decision about which features and dimensions of the social body have to be measured and monitored. New metrics of the social body such as geolocation and other metadata are key for the predictive models of the pandemic, but also for encoding labour, productivity and education. The workshop will address the deep political history of these metrics and taxonomies and their increasing implementation in the automation of the job market.

The workshop is organised by the KIM research group at HfG Karlsruhe in collaboration with the Max Planck Institute for the History of Science (MPIWG) in Berlin. It is situated within the framework of the international research network All Models (allmodels.ai) and funded by the Volkswagen Foundation program “Corona Crisis and Beyond.”
The interdisciplinary workshop addresses PhD students and scholars from science and the humanities, in particular science and technology studies, and will take place in a hybrid format. Contributors and participants will join from both the MPIWG conference room and online (in respect of the current safety measures). The final program will be announced in early September 2021. 

To register or propose a paper write to workshop@allmodels.ai.