Admission requirements
This course is available for students of the Honours College Humanities Lab.
Students in the first year of their bachelor’s programme who achieve good academic results and are very motivated, may apply for a place in Humanities Lab.
Description
Digital information technologies are profoundly affecting life in the 21st century. They are changing the nature of communication, commerce, marketing, entertainment, social relations, education, work, politics, and many other aspects of modern society. One particularly striking development is the deployment of smart algorithms to analyze the expanding data trails each of us is generating as we interact with digital environments. Service providers (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 interested third parties.
The course will offer a philosophical perspective on the consequences that smart algorithms (‘machine learning’) and Big data may have for our self-image as members of modern Western society. 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.
Course objectives
- Student 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.
Timetable
- Introduction: Wed Feb. 5
- Knowledge: Lecture Wed Feb. 12, Presentations Wed Feb. 19
- Agency: Lecture Fri Feb 21, Presentations Fri Feb 28
- Privacy: Lecture Wed March 5, Presentations Wed March 12
- Democracy: Lecture Fri March 14, Presentations Fri March 21
The timetables are available through My Timetable.
Mode of instruction
Lecture
Seminar
Assessment method
Assessment
The assessment methods will look as follows:
Participation: 10%
Oral presentation: 30%
Final project: 50%
Reflection report: 10%
Oral presentations are prepared and delivered by students working in teams of 2-3. The individual final project can be either a short essay (2000-2500 words), a video presentation (10-15 minutes), or an ‘automated’ Powerpoint presentation. The final project can be changed to a group project (2-3 students) upon a motivated request to the lecturer. The reflection report will typically be 400 words; a template for the report is provided. Final project and reflection report are due April 25, 2025.
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.
Weighing
As shown above
Attendance
Attendance is compulsory for all meetings (lectures, seminars, excursions, etc.). If you are unable to attend, notify the lecturer (listed in the information bar on the right) in advance. Being absent may result in lower grades or exclusion from the course.
Resit
A resit is offered only for the final project and the reflection report.
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.
Reading list
Required reading:
- All required readings will be made available through Brightspace.
Recommended reading:
Boden, Margaret A. (2018), Artificial intelligence. A very short introduction (Oxford: Oxford UP).
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.
Zuboff, S. (2019), The age of surveillance capitalism. London: Profile Books.
Registration
Students participating in this module will be enrolled in MyStudymap by the Education Administration Office of Humanities Lab. Students can register for the Humanities Lab modules about two to three weeks before the start of the module through an online form. On this form students indicate the modules in order of their preference. The coordinators assign students to a module based on their preference and bachelor’s programme, in order to create a diverse group of students and equal amount of students per module. Usually students get assigned to the module of their first or second choice. More information and the link to the form will be provided by Umail.
Contact
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Education Administration Office Huizinga
Remarks
This course is part of the Humanities Lab programme, visit the website for more information.
Visit the Honours Academy website for more information about the Honours College.