This course is available for students of the Humanities Lab.
If you have received your propaedeutic diploma within one academic year, your academic results are good and you are a very motivated student, you may apply for a place in the Humanities Lab.
Digital information technologies profoundly affect life in the 21st century. One particularly striking development is the use of smart algorithms to analyze the expanding data trails each of us is generating as we interact with digital environments. Providers of digital services (commercial parties as well as governments and others) routinely harvest this ‘behavioral surplus’ for a wide range of purposes: to better serve their clients, to enhance the client experience, to identify potential needs or dangers, to predict market developments, to nudge clients towards desirable behavior, or simply to collect data that can be sold to third parties.
The course discusses some important ways in which smart algorithms (‘machine learning’) and big data seem to compromise the received view of rational agency. The course is organized around four groups of themes, for each of which there will be two meetings (lecture + student presentations):
knowledge: epistemology, epistemic transparancy and opacity, explanation, justification;
agency: personal identity and autonomy, free will, commitment, responsibility;
privacy: legal aspects (including GDPR), individual vs. group privacy;
liberal democracy: political theory, representation, justification, intentionality.
- Students deepen their understanding of key aspects of our self-understanding as human beings, including in particular knowledge, agency, identity, free will, commitment, responsibility, privacy and intentionality.
- Students acquire knowledge of basic aspects of machine learning and artificial intelligence.
- Students learn how to critically assess the impact of Big data on core aspects of society.
- Students acquire and practice skills in critical analysis, argumentation, team work, and presentation.
The timetables are available through My Timetable.
Mode of instruction
The final grade is the weighted average of the following components:
Oral presentation: 30%
Final project: 50%
Reflection report: 10%
Oral presentations will be prepared and delivered by students working in small groups. The presentation schedule will be discussed at the first meeting. The final project can be either a short essay (2000-2500 words), a video presentation (10-15 minutes), or an ‘automated’ Powerpoint-style presentation with narration. The final project is an individual assignment, but can be changed into a group assignment upon a motivated request. A template will be provided for writing the reflection report.
It is not required to successfully complete all partial exams in order to pass this course. Students are allowed to compensate a ‘fail’ (grades up to and including 5.0).
The assessment methods will be further explained in the first session of the class.
To be announced.
Inspection and feedback
How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.
All required readings are available online through Brightspace.
Zuboff, S. (2019), The age of surveillance capitalism. London: Profile Books.Harari, Y.N. (2016), Homo deus. A brief history of tomorrow. London: Harvill Secker.
Weinberger, D. (2012), Too big to know. New York: Basic Books.
Students of the Humanities Lab will be registered in uSis by the administration of the Humanities Lab. More information about registration for courses will be provided by email.
General information about uSis is available on the website.
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Humanities Lab Office: email