We invite submissions to an open panel entitled « Moralizing the data economy » (panel #112) at the joined EASST and 4S (Society for Social Studies of Science) annual meeting which will be held in Prague, 18-21 August 2020 (). The deadline for submitting proposals is February 29th.
- Thomas Beauvisage, Orange Labs
- Mary Ebeling, Drexel University
- Marion Fourcade, University of California, Berkeley
- Kevin Mellet, Orange Labs & LISIS (University Gustave Eiffel University)
Panel #112: Moralizing the data economy
The new economy of data operates digital traces and tracking tools at scale, combined with the use of big data and AI technologies. Data are turned into valuable assets and tradable products, and injected into market and organizational infrastructures and practices in various industries, such as marketing, health, finance or transportation.
The industrialization of data has raised a series of concerns about its legitimacy or its morality. Data practices are disputed from a wide variety of grounds: privacy concerns; the emergence of a surveillance society; discrimination and filter bubbles; consumer manipulation; rise of new monopolies. As a reaction to these critics, new regulations (such as the GDPR in Europe) are put in place, and the players of the data economy themselves have come to incorporate moral considerations and discourses in their practices. All these views on how data should or should not be used for business and market purposes draw the boundaries of a new moral economy of data.
This panel aims to bring together empirical or theoretical contributions that explore the various facets of the moral economy of data. We particularly – though not exclusively – welcome contributions on the following topics: protest movements and civil resistance to the emerging data economy; new regulatory regimes around personal data that are held by public administrations or by corporations; the rise of market intermediaries dedicated to the moralization of the data economy; changes inside organizations and justifications surrounding the economization of data.