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Big data: Philosophical perspectives on the algorithmic turn


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.
Skills: Researching, Analysing, Project-based working, Digital skills, Collaborating, Presenting, Societal awareness, Reflecting.

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.


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:

  1. 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.
  2. Students acquire knowledge of basic aspects of machine learning and artificial intelligence.
  3. Students learn how to critically assess the impact of Big data on core aspects of society.
  4. Students acquire and practice skills in critical analysis, argumentation, team work, and presentation.

Programme and timetable:

The sessions of this class will take place on Wednesdays and Fridays, 17.15 – 19.00h

Session 1: Introduction: Lecture, Wednesday 25 October (Lipsius building, room 1.21)
Session 2: Knowledge: Lecture, Wednesday 8 November (Lipsius building, room 2.06)
Session 3: Presentations, Wednesday 15 November (Lipsius building, room 2.06)
Session 4: Agency: Lecture, Friday 17 November (Lipsius building, room 1.21)
Session 5: Presentations, Friday 24 November (Lipsius building, room 1.21)
Session 6: Privacy: Lecture, Wednesday 29 November (Lipsius building, room 2.06)
Session 7: Presentations, Wednesday 6 December (Lipsius building, room 2.06)
Session 8: Democracy: Lecture, Friday 8 December (Lipsius building, room 1.21)
Session 9: Presentations, Friday 15 December (Lipsius building, room 1.21)

Final project and reflection report are due Jan. 22, 2024.


Reading list:

Required reading
Zuboff, S. (2019), The age of surveillance capitalism. London: Profile Books.
Additional readings will be made available through Brightspace.

Recommended reading
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.

Course load and teaching method:

This course is worth 5 ECTS, which means the total course load equals 140 hours:
Seminar sessions: 10 x 2 = 20 hrs.
Required reading: 60 hrs.
Preparation of presentation: 16 hrs.
Final project: 40 hrs.
Reflection report: 4 hrs.
Total: 5 EC = 140 hrs.

Assessment methods:

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’ Prezi or Powerpoint-style 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.

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 21 August 2023 up to and including Tuesday 12 September 2023 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.

Dr. Jan Sleutels: