ASRK22108U Algorithmic Governance

Volume 2023/2024
Education

Full-degree students enrolled at the Department of Political Science, UCPH

  • MSc in Political Science
  • MSc in Social Science

 

Full-degree students enrolled at the Faculty of Social Science, UCPH 

  • Master Programme in Social Data Science
  • Master Programmes in Anthropology 
  • Master Programmes in Economics

 

The course is open to: *MA students only

  • Exchange and Guest students from abroad*
  • Credit students from Danish Universities*
  • Open University students*
Content

Algorithms are increasingly being used by governments, businesses and NGOs in ways that deeply shape people’s lives, and structure society. This may be in security and policing, in a myriad of government and non-government services, in healthcare and welfare, in traveling and leisure, and in the labor market. Algorithms nowadays do this in a way that transcends established categories and modes of governance, and thus call for new ways of thinking about how social relations can be regulated and ordered. This course will review some of the different fields in which algorithms are making their mark, and analyze the different ways in which society is being structured. This will be done by examining: the specific AI technologies that are being applied, the agents that develop and deploy them, their effects on different stakeholders, and their context in general. The course will provide students with knowledge of algorithmic capabilities, their effectiveness and strong suits, their weak spots and ethical challenges, and their cumulative effects on large groups. This will be carried out through an in-depth analysis of concrete examples from the present and the recent past. 

Learning Outcome

Knowledge:

Upon completing this course, students will be familiar with algorithms both technically, socially and ethically, and understand how they operate differently in a variety of settings.

Skills:

Students will be able to critically analyze algorithmic projects, in light of current scholarship in the field, and assess how an amalgamation of such projects may structure reality.

Competences:

Students will be able to evaluate the strong suits, weaknesses and ramifications of algorithms, both existing and ones under development.

Coglianese C and Lehr D (2019) Transparency and algorithmic governance. Administrative Law Review 71(1): 1–56.

Danaher J, Hogan MJ, Noone C et al. (2017) Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society 4(2). DOI: 10.1177/​2053951717726554.

Gritsenko D and Wood M (2022) Algorithmic governance: A modes of governance approach. Regulation & Governance 16(1): 45–62. DOI: 10.ps:/​/​doi.org/​10.1111/​rego.12367.

Issar S and Aneesh A (2022) What is algorithmic governance? Sociology Compass 16(1): e12955. DOI: 10.ps:/​/​doi.org/​10.1111/​soc4.12955.

Just N and Latzer M (2017) Governance by algorithms: Reality construction by algorithmic selection on the internet. Media, Culture and Society 39(2): 238–258. DOI: 10.1177/​0163443716643157.

Katzenbach C and Ulbricht L (2019) Algorithmic governance. Internet Policy Review 8(4). DOI: 10.14763/2019.4.1424.

König PD (2020) Dissecting the algorithmic leviathan: On the socio-political anatomy of algorithmic governance. Philosophy & Technology 33(3): 467–485. DOI: 10.1007/​s13347-019-00363-w.

Lecture, student-led discussion, student presentations, and group work.
  • Category
  • Hours
  • Class Instruction
  • 28
  • Total
  • 28
Oral
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Written examination
Type of assessment details
Free written assignment
Marking scale
7-point grading scale
Censorship form
No external censorship
Re-exam

- In the semester where the course takes place: Free written assignment

- In subsequent semesters: Free written assignment

Criteria for exam assesment
  • Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
  • Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
  • Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner