Deze informatie is alleen in het Engels beschikbaar.
Topics: Knowledge, rational agency, personal and group identity, privacy, GDPR, free will, choice architecture, political representation, liberal democracy
Disciplines: Computer science, law, psychology, political science, as well as philosophy and other humanities disciplines
Admission requirements:
This course is an (extracurricular) Honours Class: an elective course within the Honours College programme. Third year students who don’t participate in the Honours College, have the opportunity to apply for a Bachelor Honours Class. Students will be selected based on i.a. their motivation and average grade.
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:
Upon successful completion of this course, students will:
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;
acquire knowledge of basic aspects of machine learning and artificial intelligence;
learn how to critically assess the impact of Big data on core aspects of society;
acquire and practice skills in critical analysis, argumentation, team work, and presentation.
Programme and timetable:
The sessions of this class will take place from 17:15-19:00 on the following Wednesdays and Fridays:
Session 1: Wednesday, November 5
Introducstion
Session 2: Friday, November 7
Knowledge
Session 3: Friday, november 14
Presentations
Session 4: Wednesday, November 19
Lecture: Agency
Session 5: Wednesday, November 26
Presentations
Session 6: Friday, November 28
Lecture: Privacy
Session 7: Wednesday: December 3
Presentations
Session 8: Friday, December 5
Lecture: Democracy
Session 9: Friday, December 12
Presentations
The final project is due on Jan. 23, 2026.
The reflection report is due Jan. 28, 2026.
Location:
Lipsius building, room 2.12
Reading list:
All require reading will be announced in class or via 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.
Course load and teaching method:
This course is worth 5 ECTS, which means the total course load equals 140 hours:
Seminars: 9 seminars of 2 hours (participation is mandatory)
Literature reading: 8 hours/week, 7 weeks
Presentation: 20 hours
Final project: 40 hours
Assessment methods:
The assessment methods will be further explained in the first session of the class.
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.
The final project is due on Jan. 23, 2026, and the reflection report is due Jan. 28, 2026.
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.
Brightspace and uSis:
Brightspace will be used in this course. Upon admission students will be enrolled in Brightspace by the teaching administration.
Please note: students are not required to register through uSis for the Bachelor Honours Classes. Your registration will be done centrally.
Application process:
Submitting an application for this course is possible from Monday 25 August 2025 up to and including Sunday 7 September 2025 23:59 through the link on the Honours Academy student website.
Note: students don’t have to register for the Bachelor Honours Classes in uSis. The registration is done centrally before the start of the class.
Contact:
Dr. Jan Sleutels: j.j.m.sleutels@hum.leidenuniv.nl