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Big data: Philosophical perspectives on the algorithmic turn


Disclaimer: due to the coronavirus pandemic, this course description might be subject to changes.

Topics: empirical science, knowledge, 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: Research, conceptual analysis, argumentation, team work, presentation skills.

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:

Wednesdays and Fridays, 5-7 PM
1. Introduction: Wed Oct. 28
2. Knowledge: Lecture Fri Oct. 30, Presentations Fri Nov. 6
3. Agency: Lecture Wed Nov. 11, Presentations Wed Nov. 18
4. Privacy: Lecture Fri Nov. 20, Presentations Fri Nov. 27
5. Democracy: Lecture Wed Dec. 2, Presentations Dec. 9
6. Closing session: Dec. 11


All sessions will take place online through Kaltura. If possible, the closing session will take place at one of the locations of Leiden University.

Reading list:

Required reading
Zuboff, S. (2019), The age of surveillance capitalism. London: Profile Books.
Additional readings tba.

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.
Written self-assessment: 4 hrs.
Final project: 40 hrs.
Total: 5 EC = 140 hrs.

Assessment methods:

The assessment methods will look as follows:
The final grade is the weighted average of the following components:
Participation: 10%
Oral presentation: 30%
Written self-assessment of personal growth as an academic : 10%
Final project: 50%

Oral presentations will be prepared and delivered by students working in pairs. The presentation schedule will be discussed at the first meeting. The written self-assessment should typically be 400 words. 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.

For 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 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.

Registration process:

Enrolling in this course is possible from 17 August 2020 up to and including 3 September 2020 through the Honours Academy. The registration link will be posted on the student website of the Honours Academy.

Dr. Jan Sleutels: