Data Governance: Law, Ethics and Politics
Course content
The increasing datafication of the world brings with it a range of ethical, legal, and political challenges. From the ethics of data privacy to legal frameworks such as GDPR and legislation regulating tech giants, new data governance issues surface rapidly. This course introduces students to key legislation, as well as political and ethical debates concerning the governance and security of data. Students are taught how to make data collection and processing ethically sound and legally compliant, providing them with the necessary knowledge, skills, and competencies regarding data protection and data management, complementing the social data science and programming skills they acquire on the other courses on the Master’s degree programme in Social Data Science.
Full-degree students enrolled at the Faculty of Social Science, UCPH
- MSc in Security Risk Management
- Master Programme in Social Data Science
- Master Programmes in Sociology
- Master Programmes in Psychology
- Master Programmes in Anthropology
- Master programme in Political Science and Social Science
- Master Programmes in Economics
Mandatory course on MSc programme in Social Data Science at University of Copenhagen.
At the end of the course, students are able to:
Knowledge:
- Account for ethical, legal, and political aspects and consequences of the collection and use of data for a range of administrative, scientific and commercial purposes.
- Explain key legal and social science concepts, ideas, and debates pertaining to the use of different types of data in private and public contexts, including the ethical debates regarding the use of algorithms and machine learning (e.g. FATE: Fair, Accountable, Transparent, Ethical).
- Demonstrate insight into the content and implications of national and EU legal frameworks for data collection, processing and storage (i.e. GDPR).
Skills:
- Evaluate the quality of own as well as other people’s use of methods, datasets and analytical approaches in relation to the ethical, legal and political aspects or data protection.
- Commuinicater central questions around data ethics – academic as well as policy-oriented – peers and non-experts.
- Formulate efficient, ethically and legally sound procedures for managing data, including data stewardship, ownership, compliance, privacy, data risks, data sensitivity and data sharing.
Competences:
- Navigate and understand existing key legislation, rules, and ethical frameworks for personal data management and governance, especially GDPR.
- Critically discuss possibilities and risks associated with the use of data when implementing data governance policies and rules in organizations and institutions based on frameworks from social science and law.
- Assess concrete cases of data governance, including the identification of problems, risks of misuse, as well as the benefits of data analysis.
The course combines lectures, workshops, quizzes, group exercises, student presentations, and peerfeedback seminars. Expert guest lecturers will be invited to give talks, especially in connection to aspects of the course related to GDPR and Danish data protection legislation.
500 pages mandatory, plus 200 pages of non-mandatory literature, selected by the teachers. Literature will be in the form of articles, book chapters, blog posts, guidelines, and policy documents.
Students will be registered by the student administration.
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
On-site written exam, 4 hours
- Type of assessment details
- On-site exam with a set of questions and problems that need to
be
answered, revolving around data law, data ethics, and the management
of data-sets.
Individual. - Examination prerequisites
-
3 out of the 4 assignments must be approved for the student to
participate in the exam. - Aid
- Only certain aids allowed (see description below)
Specific aids will be posted here prior to the registration period for the Spring semester.
ChatGPT and other large language model tools are permitted as a dedicated source, meaning text copied verbatim needs to be quoted, the tool cited, and generally the specific use made of them needs to be described in the submitted exam.
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
The exact time and place will be available in Digital Exam from the middle of the semester.
- Re-exam
-
The reexam will differ from the ordinary examination in two respects: 1) in the first essay, students must answer a substantially new problem statement of their own choosing; 2) the dataset supplied for the second essay will differ from the one from that was handed out in connection with the ordinary examination.
Either individual or in groups. Individual submissions must not exceed 10 pages (app. 5 pages per home assignment).
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
- Category
- Hours
- Lectures
- 28
- Preparation
- 56
- Exercises
- 42
- Project work
- 80
- English
- 206
Kursusinformation
- Language
- English
- Course number
- ASDK20003U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 3
- Studyboard
- Social Data Science
Contracting department
- Social Data Science
- Department of Anthropology
- Department of Psychology
- Department of Political Science
- Department of Sociology
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinators
- Kristoffer Langkjær Albris (17-7279707a7b766d6d6c793568736979707a477a766b687a35727c356b72)
- Yani Kartalis (10-776d7e806d78757f75714c7f7b706d7f3a77813a7077)
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Kursusinformation for indskrevne studerende