In the last 50 years, research on artificial intelligence (AI) has repeatedly boomed but failed to deliver on its great promises. In the last decade, however, especially the deep learning approach has achieved remarkable results and is already applied in many contexts. Since this approach breaks with assumptions of the older symbolic approaches of AI research, a new philosophical discussion is needed. Therefore, the interdisciplinary seminar will start from the classical philosophical debate, which was shaped by thinkers like Herbert Dreyfus and John Searle and focused on the concept of the rule following, in order to confront it with the newer state of research, its data driven approach and the concept of learning. We will discuss the consequencesand challenges of these new approaches in AI for their theoretical and philosophical reflection.
In a second step, the seminar will discuss not only epistemological, but also ethical and political aspects of the recent developments in AI in interdisciplinary and international perspectives. We plan to discuss the following topics:
- Natural Science: How are the sciences changing by employment of AI-technologies?
- Society: How does AI change the structure of our society?
- Politics: How does AI transform our political landscape, and how does it threaten it?
- Economics: How does AI transform economics, using the example of Amazon?
- Ethics: What are the dangers and chances of a data-driven technology (data bias, dataprivacy)? How can AI contribute to a better world, and how does it threaten it?
— Dreyfus, Hubert L. (1972): What Computers Can't Do. New York: MIT Press. Searle, John (1984): Minds, Brains and Science. Cambridge, Mass: Harvard University Press.
— Collins, Harry and Martin Kusch (1999): The Shape of Actions. What Humans and Machines Can Do. Cambridge, Mass: The MIT Press.
— Buckner, Cameron (2019): Deep learning: A philosophical introduction, in: Philosophy Compass 14, https://doi.org/10.1111/phc3.12625.
— Schubbach, Arno (2019): Judging machines: philosophical aspects of deep learning”, in: Synthese (online first), https://doi.org/10.1007/s11229-019-02167-z.
— Campolo, Alexander and Kate Crawford (2020): Enchanted Determinism: Power without Responsibility in Artificial Intelligence, in: Engaging Science, Technology, and Society 6: 1-19, https://doi.org/10.17351/ests2020.277.
— Nassehi, Armin (2019): Muster. Theorie der digitalen Gesellschaft. München.
— Noller, Jörg (Ed) (2020): Luciano Floridi, The Green and The Blue: A New Political Ontology for a Mature Information Society, in: Philosophisches Jahrbuch 127/2.
- Teacher: Jörg Noller