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This conference on digital memories brings together the most current research from various fields such as media design, cultural studies and philosophy, representing both theoretical and empirical approaches. We will explore the concept of trust, and the many ways it is related to memory in online practices and interactions. With social media, digital archives and self-tracking apps being new means we use to create and share memories, it is important to ask how and why we tend to trust online platforms and digital devices as repositories of our past. Furthermore, do we tend to trust digital memory more than biological memory, considering it to be more objective and stable? According to Victor Mayer-Schönberger: “As digital remembering relentlessly exposes discrepancies between factual bits and our very own human recall, what we may lose in the process is the trust in the past as we remember it.” What are the consequences of this potential shift for the way we make sense of ourselves through our past? How individuals and collectives deal with the increasing volume of their digital memories, as well as with new ways of remembering and forgetting through the use of technology, is a challenging question, the answer to which requires an interdisciplinary approach. Therefore, this conference’s aim is to gather contributions from variety of disciplines in order to further the discussion on this developing field of study.

Organized by: Serena Ciranna (Institut Jean Nicod, Paris), Gloria Origgi (Institut Jean Nicod, Paris)

Scientific Committee: Roberto Casati (Institut Jean Nicod, Paris), Serena Ciranna (Institut Jean Nicod, Paris), Jerôme Dokic (Institut Jean Nicod, Paris), José Van Dijck (Utrecht University), Gloria Origgi (Institut Jean Nicod, Paris), Judith Simon (Hamburg University)

With the sponsoring of

DEC- Département d’Etudes Cognitives, ENS

EHESS - Ecole des Hautes Etudes en Sciences Sociales

The Internet of Things Chair - ESCP Europe - Business School

 

 

 

 

 

 

 

 

 

 

Attached documents


EHESSCNRSENS