Postgraduate Course: Datafication, Accountability and Democracy (fusion online) (EFIE11075)
|School||Edinburgh Futures Institute
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Available to all students
|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 and economic aspects of data and digital technologies to explore concepts such as democracy, transparency, accountability, design justice and refusal.
This course introduces students to theories of democracy 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, design justice and refusal in both democratic and more authoritarian contexts.
This course will be taught in a hybrid format. Initially (pre 2-day intensive), students will be asked to read a selection of core course texts and record reflections on readings in a 500-word count reflection (formative only) that they will share and discuss with other students in assigned groupings. Student groupings will also be asked to examine an assigned case study of a democratically designed digital tool or platform; they will be asked to present their case study during the first day of the 2-day intensive session.
The 2-day intensive session will combine live-streamed lectures, discussion carried out both in-person and over a digital platform such as Teams (for online students) and group work conducted in-person and on a digital platform such as Teams. On the second day, student groups will select another case study, this time on experiments in platform or algorithmic governance and oversight.
During the post 2-day intensive students will synthesise the concepts and skills learned in the course. Each group will give an online presentation of their case-study analysis and enter it into a course database on algorithmic governance. Students will also write a final essay (on a topic they will clear with the course organiser), to produce original pieces of work demonstrating the theoretical and practical skills they acquired.
Edinburgh Futures Institute (EFI) - Online Fusion Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. 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 note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.
As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2023/24, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Lecture Hours 5,
Seminar/Tutorial Hours 14,
Other Study Hours 16,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Additional Information (Learning and Teaching)
Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 16
|Assessment (Further Info)
|Additional Information (Assessment)
The course will be assessed by means of the following assessment components:
1) Group Presentation (30%)
Students will work in groups to create a presentation around their additions to the course database on civic models of algorithmic and platform governance. These presentations will be given online, either synchronous or asynchronous (30%).
2) 1500 Word Essay (70%)
Each student will produce a final 1500 word essay drawing on course theories, due two weeks after the intensive period (70%).
||Feedback on the 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 will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
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 prior to the intensive period. Staff will provide immediate feedback on group presentations. Staff will also be on hand to work with students to outline their final paper.
|No Exam Information
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 STS, 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 systems, activism and civic participation, and design justice and values in design.
- Understand how to work alongside other students by using group-work to contribute to a database of 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.
|Indicative Reading List:|
Barassi V. Datafied Citizens in the Age of Coerced Digital Participation. Sociological Research Online. 2019;24(3):414-429. doi:10.1177/1360780419857734
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197-208. https://doi.org/10.24908/ss.v12i2.4776
Micheli M, Ponti M, Craglia M, Berti Suman A. Emerging models of data governance in the age of datafication. Big Data & Society. July 2020. doi:10.1177/2053951720948087
Ananny M, Crawford K. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society. 2018;20(3):973-989. doi:10.1177/1461444816676645
Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.
Robert Gorwa (2019) What is platform governance?, Information, Communication & Society, 22:6, 854-871, DOI: 10.1080/1369118X.2019.1573914
Lina Dencik, Fieke Jansen and Philippa Metcalfe. 2018. A conceptual framework for approaching social justice in an age of datafication (working paper). Cardiff University.
Barocas, S., Hood, S. and Ziewitz, M. (2013) 'Governing Algorithms: A Provocation Piece', in 'Governing Algorithms' Conference, New York University, pp. 1-12. doi: 10.2139/ssrn.2245322.
Citron, D. K. and Pasquale, F. (2014) 'The Scored Society: Due Process for Automated Predictions', Washington Law Review, 91(1), pp. 1-33.
Sasha Costanza-Chock. Design Justice: Community-Led Practices to Build the Worlds We Need. MIT Press. 2020.
|Graduate Attributes and Skills
||1) Students will develop key theoretical knowledge and critical understanding through readings, discussion, case study analysis and reflections on core texts (SCQF characteristic 1 & 2).
2) Students will gain cognitive skills by conducting original research on two case studies, analysed in relation to the course literature and themes and added to the course database (SCQF characteristic 2).
3) Students will develop communication skills by interacting with academic staff and their peers and by delivering a final group presentation to the class (SCQF characteristic 3 & 4).
4) Students will gain autonomy, accountability and learn to work with others by collaborating in small groups on the case study and during the preparation stage of their project, developing their communication skills, and gaining valuable skills in working with others (SCQF characteristics 3& 4).
|Keywords||Democracy,Participation,Accountability,Datification,Big Data,Algorithmic Governance,Values in Design
|Course organiser||Dr Morgan Currie
Tel: (0131 6)50 6394
|Course secretary||Mr Lawrence East