Postgraduate Course: Controversies in the Data Society (PGSP11467)
Course Outline
| School | School of Social and Political Science |
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 | The extraordinary growth and pervasiveness of born-digital 'big data' from administrative and commercial systems, and the deployment of 'data science' is generating fierce controversy. For example, over the unprecedented dataveillance powers provided by the UK Investigatory Powers Act or current profiling and sorting of US immigrants using social media data; and the protests, court cases and legislation around algorithmically-mediated markets such as Uber or Airbnb.
Students from a range of disciplinary backgrounds are increasingly expected to engage with these controversies in their research, whether it be in developing and applying new data science approaches, legal opinion, or in the analysis of business, governance and society.
This course will enable participants to study alongside students from other disciplines, addressing cutting edge controversies led by experts in a range of fields, with a focus on empirical cases. It will complement discipline-oriented course, and provide an opportunity to develop cross-disciplinary research projects.
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| Course description |
This will be an interdisciplinary seminar course, drawing together students from different backgrounds in the emerging field of 'Data and Society' with each session led by a different expert from within the University, focused primarily on current empirical cases in which the use of new and emerging forms of data, and industrial scale data science are generating controversy.
Examples of controversies that highlight political, ethical and business conflict in fields include : the use of mass surveillance data by governments; Linking of health records for research; Calculation of individual medical and insurance risk from behavioural and genetic data; Use of predictive techniques in policing; Algorithmic social sorting and discrimination in recruitment; and Psychometric profiling and targeting in political campaigning;
The value of this approach is that these topics are inherent multi-dimensional and open to analysis from different disciplinary perspectives. Many of the topics are relatively new and emerging, and conventional literature has not engaged with the latest debates, especially when they are focused on contemporary events. The University has a range of leading researchers who work directly on current issues, with agencies, business, NGOs and policy makers able to expose students to the latest evidence and analysis.
The aim is to facilitate an multi-disciplinary cohort of students from a range of backgrounds to take concepts and approaches they have learnt in previous courses in their own fields of study,, from STS, data science, digital sociology, politics law etc, to understand how and why these controversies have arisen, the evidence available, the conflicting positions of stakeholders, and ongoing attempts and approaches to resolve them.
This will equip students with skills to be able to foresee and prepare for controversial situations in research, design, technical deployment and governance, and to be able to conduct research and development activities around these themes and debates.
Class leaders will be encouraged to set the classes up as debates, that enable current controversies (e.g. a new Government Bill, a court case, a regulatory investigation etc) to be explored and informed by reading of existing literature.
The course design draws on the model of an existing successful STIS course: Controversies in Medicine, Technology & Environment. The course will explicitly support the University strategic goals in Data Science, and facilitate cross-school research collaborations.
Below is an initial list of staff who have agreed to contribute.
Burkhart Shafer (Law) - Evidence
Charles Raab (SPS) - Government surveillance
Richard Jones (Law) Policing
James Stewart - Big Data industry influence on policy
Cate Heeney (STIS) - Access to Biobanks
Karen Gregory- Labour
Joyce Tait - Stratified Medicine and Linked Data
Claudia Pagliari (Med) - e-health privacy
Ewa Luger - (ECA/INF) Accountability through Blockchain
Stuart Anderson (Informatics) and Lilian Edwards (STIS/Law) Algorithmic Governance
Kami Vaniea (Inf) Data and democracy
Paolo Cavaliere (Law) - Internet Platforms and Fake News
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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 2019/20, Available to all students (SV1)
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Quota: 22 |
| Course Start |
Semester 2 |
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) |
The assessment will consist of a formative and a summative assessment. The Formative assessment will be 20% of the course, and will consist of a presentation in class, or production of a video for discussion. The Summative assessment will be a long essay weighted 80%. The format of the essay will be determined in relation to the disciplinary home of the student, and may be a reflection on a current issue, a theoretical synthesis, or reflection and planning for a research project.
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| Feedback |
The students will participate actively in debate in the seminar classes, received immediate feedback. Bi-weekly Seminar classes will enable more detailed feedback and discussion of topics to ensure understanding is developed. A formative assessment based on a presentation or video will receive formal feedback. Staff will support students in the development of a final essay topic, including viewing plan and readings. Students will be supported using the usual online tools.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply a Critical understanding of a range of contemporary social and political controversies provoked by use of data and data science.
- Engage with a range of conceptual tools that can be used to analyse data-centred controversies.
- Understand the main dimensions of emerging data practices that provoke controversy, including legal frameworks, technical tools (machine learning, data infrastructure etc), and cultural practices.
- Be equipped to identify, foresee and prepare for controversial situations in research, design, deployment and governance that implicate the use of Data and Data Science.
- Develop post-graduate research projects on Data and Society that are sensitive to cross-disciplinary issues, and be able to engage with peers and senior colleagues in developing research.
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Reading List
| Each seminar leader will provide compulsory readings in advance. |
Additional Information
| Graduate Attributes and Skills |
Ability to work in an interdisciplinary group to analyse multi-dimensional controversies, to bring their own expertise, to collaborate with others with complementary expertise and gain benefit from appreciating these diverse contributions.
Communicate with peers, more senior colleagues and specialists, their expertise on topics covered in the course.
Communicate the issues covered in the courses to public audiences.
Manage complex ethical and professional issues and make informed judgements on issues not addressed by current professional and/or ethical codes or practices.
Critically review, consolidate and extend knowledge, skills, practices and thinking in the fields of Data and Society.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr James Stewart
Tel: (0131 6)50 6392
Email: J.K.Stewart@ed.ac.uk |
Course secretary | Mr Dave Nicol
Tel: (0131 6)51 1485
Email: dave.nicol@ed.ac.uk |
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