Shared Uncertainty and Individual Prediction. Does Algorithmic Forecast Affect the Open Future?


The spread of learning algorithms is changing the meaning and forms of prediction, affecting the image of the future and the way to deal with it in the present. Whereas in the modern view the future is seen as open and unknowable because it does not yet exist and depends on present actions and expectations, today’s predictive algorithms claim to foresee the future providing an individual score for singular persons or events. But knowing the future in advance is not only advantageous. In fact, for our society, shared uncertainty about the future is also a resource. What happens to the stabilized forms of management of the future as a consequence of digital techniques? 

Elena Esposito is Professor of Sociology at the University of Bielefeld and the University of Bologna. A leading figure in sociological systems theory, she has published extensively on the theory of society, media theory, memory theory and the sociology of financial markets. Her current research on algorithmic prediction is supported by a five-year Advanced Grant from the European Research Council. Her latest book Artificial Communication: How Algorithms Produce Social Intelligencewill be published in 2021 by MIT Press.

Moderation: Professor Dr. Eckhard Schumacher

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