Postgraduate Course: Datafication, Accountability and Democracy (EFIE11480)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | What does it mean to foster democratic governance of digital platforms and algorithmic systems? How can concepts of democracy and accountability drive the design of technologies so that they embed democratic agendas?
This course will examine the political-economic and infrastructural aspects of data and digital technologies to explore concepts such as democratic accountability, transparency, public trust, and values-based design. |
| Course description |
This course introduces students to theories of democratic data governance and explores the relationship between public accountability, big data and digital platforms. While digital platforms may help democratic institutions flourish, many of today's digital and data-driven infrastructures are controlled by commercial companies or governments that do not have democratic values in mind.
In this context, what does it mean to foster democratic governance of digital platforms and algorithmic systems? Relatedly, how have concepts of democracy and accountability driven the design of technologies so that they embed democratic agendas in the first instance?
This course will examine the political and economic aspects of data and digital technologies to explore concepts such democracy, transparency, accountability, and social justice in both democratic and more authoritarian contexts
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities.
Students should be aware that:
- Classrooms used in this course will have additional technology in place: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, physical keyboard, and internet access
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: 30 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course will be assessed by means of the following components:
1) Group Presentation (10%)
Learning Outcomes Assessed by Component: 1, 2, 3, 4
2) Biography of an Artifact (30%)
1,000 word 'white paper' evaluating a tool, technique or campaign related to AI governance.
Learning Outcomes Assessed by Component: 1, 2, 4
3) Critical Review Essay (60%)
2,500 word critical review essay (which can build on the white paper).
Learning Outcomes Assessed by Component: 1, 2, 4 |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Academic staff will give feedback to each student's reflections on core readings, and group presentations, prior to final essay. Feedback will be returned on the reflections on the readings. Staff will provide immediate feedback on group presentations. Staff will also be on hand to work with students to outline their final paper.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate discerning understanding of key course concepts drawing on theories of democratic governance, critical data studies, media studies and Science and Technology Studies, as supported by reading relevant literature.
- Synthesise a variety of theoretical and empirical material from different disciplinary perspectives in order to tackle contemporary issues relating to platform governance, governance of algorithmic and AI systems, activism and civic participation, and values in design.
- Understand how to work alongside other students by using group-work to contribute case studies relevant to the course.
- Be able to communicate complex ideas pertaining to the political and social dimensions of digital and data-driven technologies.
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Reading List
Zuboff, Shoshana. 2019. Ch. 3: "The Discovery of Behavioural Surplus." The Age of Surveillance Capitalism: the Fight for the Future at the New Frontier of Power / Shoshana Zuboff. London: Profile Books. Download available through the library.
Viljoen, Salome. 2021. A Relational Theory of Data Governance. The Yale Law Journal 131 (2): 573-654. https://www.yalelawjournal.org/pdf/131.2_Viljoen_1n12myx5.pdf
Robert Gorwa. 2019. "What is platform governance?" Information, Communication & Society, 22:6, 854-871, DOI: 10.1080/1369118X.2019.1573914
Donald MacKenzie. 2022. 'Blink, Bid, Buy Donald MacKenzie on Digital Advertising and Online News.' London Review of Books 44 (9). https://www.lrb.co.uk/the-paper/v44/n09/donald-mackenzie/blink-bid-buy.
Scott, R. M.-G., & Edwards, L. (2025). The Inscrutable Code? The Deficient Scrutiny Problem of Automated Government. Technology and Regulation, 2025, 37-59. https://doi.org/10.71265/sxb9dj82.
Read ch. 1 "Bomb Parts." O'Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy / Cathy O'Neil. First paperback edition. Broadway Books; 2017. Available through the library.
Read Ch. 4 "Directed Surveillance: Predictive Policing and Quantified Risk." Brayne S. Predict and Surveil: Data, Discretion, and the Future of Policing / Sarah Brayne. Oxford University Press; 2020. Available through the library.
Brown, Davidovic, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8(1), 205395172098386. https://doi.org/10.1177/2053951720983865 |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Democracy,Participation,Accountability,Big Data,Algorithmic Governance,Values in Design |
Contacts
| Course organiser | Dr Morgan Currie
Tel: (0131 6)50 6394
Email: morgan.currie@ed.ac.uk |
Course secretary | Mr Matt Bryant
Tel:
Email: Matt.Bryant@ed.ac.uk |
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