DSOC 27A18 - Social medias, algorithms and intermediate bodies
This course is an introduction to an emerging sociology of algorithms. Its aim is to provide a sociological understanding of what algorithms do to the social world and its actors, institutions, established professions and, more broadly, to democracies and intermediary bodies. One part of the course also proposes to address and understand what a certain category of algorithms produce in an authoritarian political context. This course is based on work in the sociology of work, sociology of scientific knowledge, sociology of digital technology, sociology of expertise and sociology of information. No knowledge or training in sociology is necessary to follow and understand it. It has been designed as a means of introducing students both to general sociological concepts and to their specificities in an algorithmic context.
Using scientific articles and digital supports, students will have the opportunity to acquire a sociological understanding of the theoretical, social, economic and political issues raised in recent years by big data, digital platforms (with their business models and recommendation algorithms), and certain artificial intelligence tools.
This course will look at subjects such as new forms of production and work, new ways of processing data and information on a large scale, and the transformation of established professions, intermediate bodies and the upheaval of various sectors of activity and expertise.
Mariame TIGHANIMINE
Séminaire
English
Autumn 2023-2024
Individual exercise or presentation in class (50% of the grade)
Completion of an interview or online ethnography related to one or more chapters of the course (50% of the grade)
Oral participation is encouraged and can make a positive contribution to the final mark. This also means that shy people will not be penalized for non-participation.
Berrebi-Hoffmann, Isabelle, et Quentin, Chapus. Des luttes éthiques aux luttes sociales. Les mouvements de contestation critique des salariés des GAFAM aux États-Unis (2015-2021), Réseaux, vol. 231, no. 1, 2022, pp. 71-107.
Boullier, Dominique, et El Mahdi El Mhamdi. Le machine learning et les sciences sociales à l'épreuve des échelles de complexité algorithmique. Revue d'anthropologie des connaissances 14.14-1, 2020.
Buolamwini, Joy, and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on fairness, accountability and transparency. PMLR, 2018.