One of the key elements in unsupervised learning is clustering. Thus, this particular data practice sits at the core of modern Artificial Intelligence, which is based on artificial neuronal networks. Whereas classification operates by organizing labeled data into specific categories, clustering relies on cheaper, unlabeled data for deciphering similarities inside a given set.
While many scientific disciplines might be interested in this new element of technical progress, the social sciences should be. The workshop poses the open question if unsupervised data clustering has the potential of identifying and generating new patterns of the social. This idea is not new. As Orit Halpern has remarked, attempts to break free from stable categories like race, identity, territory, or ethnicity with the help of pattern recognition can be found e.g. in the works of political scientist Karl Deutsch already in the 1960s (Halpern 2014, p. 191). Can clustering come up with entirely new orders of the social, such as tribes of movements identifiable from telephone data, do they detect political party affiliation, friendship or kinship-patterns that are not blood-related, and thus resemble totemistic orders? Or does automatization in the analysis of social data reproduce older hierarchies and familiar stratifications with necessity? While it is crucial not to fall prey to techno-utopian fantasies of non-situated (AI) technologies ‘overcoming’ race, class or gender, the transformative potential of clustering practices for analysis and reorganization of society and resource management in crisis should not be dismissed entirely.
The workshop is part of the research project HiACS, funded by one of Europes largest research funding institutions, the Volkswagen Foundation Hannover. Members are Jens Schröter and Andreas Sudmann, University of Bonn, Alexander Waibel and Fabian Retkowski KIT/Carnegie Mellon, as well as Anna Echterhölter and Markus Elias Ramsauer, University of Vienna.
Join us live or online via following Zoom links:
Thursday, 28.11.
us06web.zoom.us/j/87831389185
Meeting-ID: 878 3138 9185
Password: mcX7T5
Friday, 29.11.
us06web.zoom.us/j/87613835276
Meeting-ID: 876 1383 5276
Password: ZwhF8y
For more information and the full program visit the event website.
