Deep City: Climate Crisis, Democracy and the Digital | Call for Submissions

Big data, smart systems, machine learning – it is inevitable that these new technologies will change the way we study, build and manage our cities. At the same time resurgent interest in consensus and contributive action seems to oppose an exclusively data-driven urbanism. Is the opposition of machine intelligence and democracy inevitable, or are shared trajectories possible?

Politicians, social scientists, urbanists, and architects find their working methods and disciplinary knowledge challenged by insights derived from big data, machine learning and Artificial Intelligence (A.I.). As both citizens and experts in the many respective fields, our mission must be to bring in our disciplinary knowledge and civic engagement to support the appropriate development and discussion of data-driven tools, to consider both their biases and potentials, and to promote broader social literacy and criticality. Ultimately, it is a question of both decanting the technical quality of these new instruments of design, management and decision making, as well as enhancing democratic control over them. How do we do this?  Do our disciplines currently lack adequate strategies to understand, let alone critique or exploit the knowledge-products of machine learning, artificial intelligence and big data?

DEEP CITY Latsis Symposium will be a scientific conference staged as a social and cultural event, a dialogical field capable of moving beyond disciplinary boundaries. It will host keynotes and paper presentations, as well as a range of crossing formats such as workshops, political debates, table dinner, an exhibition and guided tours. DEEP CITY will take place on the EPFL Campus in Lausanne, from November 4th to 7th 2020. To ground this dialogical field and foster meaningful exchange, we invite proposals, ranging from operational practices to speculative and theoretical questionings. These can be presented in written, built, coded, drawn, figured, imagined, filmed, modelled or in any other way conveyed format.