301251 Society, Algorithms and Digital Transition (MA Soziologie) (BS) (WiSe 2023/2024)

Course contents
Recent algorithmic techniques based on machine learning and the use of large amounts of contextual, dynamic, and heterogeneous data (big data) are having an increasingly pervasive impact on several areas of contemporary society. The course analyzes different aspects of the ongoing transformations: the characteristics of data, the evolution of machine learning, the interpretation of artificial intelligence, the intransparency of deep learning algorithms and related problems, the predictive use of algorithms, the governance of algorithms, and the problems of responsible and fair use.

Learning outcomes
The course aims to provide the knowledge and tools to understand the social implications of the digital transition. The student at the end of the course is familiar with the main social and communicative aspects related to the development and use of artificial intelligence systems capable of autonomous learning.

Teaching methods
Participants are expected to read the materials in advance and actively contribute to the discussion. They must prepare two questions for each meeting, that can be presented and debated during the sessions. After each session, they will deliver a short memo (e.g. several bullet points) summarizing their understanding of the outcome of the meeting in terms of the issues they find most relevant.

Bibliography

Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. https://www.wired.com/2008/06/pb-theory/

Mayer-Schönberger, V. & Cukier, K. (2013). Big Data. A Revolution That Will Transform How We Live, Work, and Think. London: Murray. Chapters 1-4

O’Neil, C. (2016). Weapons of Math Destruction. New York: Crown. Introduction and Chapters 1 and 2

Esposito, E. (2022). Artificial Communication: How Algorithms Produce Social Intelligence. Cambridge: MIT Press. Introduction and Chapter 1 and Chapter 7

Weinberger, D. (2017). Machines now have knowledge we’ll never understand. Wired, April 18 https://www.wired.com/story/our-machines-now-have-knowledge-well-never-understand/

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society 1: Chapter 1-12.

Esposito, E. (2023). Does Explainability Require Transparency? Sociologica, in stampa.

Pariser, E. (2011). The Filter Bubble. What the Internet Is Hiding from You. London: Viking. Chapters 1 and 2

Cevolini, A. & Bronner, G. (2018). What is New in Fake News? The disinhibition of dissent in a hyperconnected society. Sociologia e politiche sociali: 75-92.

Rona-Tas, A. (2020). Predicting the Future: Art and Algorithms. Socio-Economic Review: 1–19.

Cevolini, A. & Esposito, E. (2020). From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance. Big Data & Society 7(2).

Burrell, J. & Fourcade M. (2021). The society of algorithms. Annu. Rev. Sociol. 47: 213–37.