301201 Society, algorithms and digital transition (BS) (WiSe 2024/2025)

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Requirements for participation, required level

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 opacity 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 can prepare 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

Kitchin, Rob (2014). Big Data, new epistemologies and paradigm shifts. Big Data and Society, 1(1)
boyd, d., & Crawford K. (2012). Critical Questions for Big Data. Information, Communication and Society 15(5): S. 662–679
Esposito, E. (2022). Artificial Communication: How Algorithms Produce Social Intelligence. Cambridge: MIT Press. Introduction and chapter 1
Busuioc M. (2020). Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review 81(5): S. 825–836
Lipton, Z. C. (2018). The Mythos of Model Interpretability. ACM Queue 16(3): S. 1-27
Esposito, E. (2023). Does Explainability Require Transparency? Sociologica 16(3): S. 17–27
Watts, D. J. et al. (2018). Explanation, prediction, and causality: Three sides of the same coin? Microsoft
Rona-Tas, A. (2020). Predicting the Future: Art and Algorithms. Socio-Economic Review: S. 1–19
Esposito, E. (2022). Artificial Communication: How Algorithms Produce Social Intelligence. Cambridge: MIT Press. Chapter 7
Bowman S. R. (2023) Eight Things to Know about Large Language Models. arXiv:2304.00612v1
Sejnowski T. (2023). Large Language Models and the Reverse Turing Test. Neural Computation 35, S. 309–342
Pütz O. & Esposito E. (2024). Performance without understanding: How ChatGPT relies on humans to repair conversational trouble. Discourse and Communication