MST 0061 Data and Technology Regulation and Governance
MST 0061 Data and Technology Regulation and Governance
This course provides comprehensive insight into the fundamental concepts of data and technology regulation, ethics, and governance in modern business. As organisations increasingly rely on advanced data analytics, AI, and algorithmic decision-making, it is no longer sufficient to solely view data through the lens of privacy compliance. This course examines the broader legal, ethical, and societal challenges posed by emerging technologies.
With a practical, business-oriented perspective, the course offers an introduction to technology governance. Students will explore how to ethically manage data and algorithms as powerful tools in modern work environments, and how to navigate key legislative frameworks such as the GDPR or the EU AI Act. Furthermore, the course introduces students to real-world engagement with policymakers, discussing modes of operation and analytical tools through a stylized lifecycle of policy advisory.
Upon successful completion of the course, students will have a comprehensive understanding of the legal and regulatory frameworks governing technology as well as ethical implications of using data and automation technologies in business.
Specifically, students will:
- Gain detailed knowledge of the evolving technology governance frameworks in the EEA and EU, moving beyond the General Data Protection Regulation (GDPR) to include frameworks governing artificial intelligence (e.g., the EU AI Act) and data markets;
- Learn the foundational concepts and terminology related to technology regulation, algorithmic accountability, data ethics, and privacy;
- Understand the ethical challenges in modern business arising from the use of big data and AI (such as surveillance, automated decision-making, and the personalization of services) and how these technologies act as either tools or agents in society;
- Gain a solid understanding of the foundations of translational research and the core elements of policy advising, including co-design processes and iterative collaboration with policymakers.
At the end of the course, the students will be able to:
- Differentiate between legitimate, ethically sound business behaviors and problematic ones in light of current and emerging rules;
- Advise on and design appropriate solutions to address regulatory and ethical concerns associated with the use of AI, algorithms, and big data in business analytics;
- Responsibly manage the use of data, AI systems and algorithms in the context of business analytics, particularly in compliance with key legislation such as the GDPR or AI Act;
- Utilize exemplary cases to inform and shape policy work, demonstrating an understanding of how to engage with policymakers;
- Engage in informed debates on the necessary balance between technological innovation, privacy, market power, and societal well-being.
At the end of the course, the students will:
- Develop heightened ethical and regulatory consciousness when assessing challenges related to data and technology;
- Recognize and navigate ambiguities beyond clearly defined regulatory boundaries;
- Be able to place rules within the broader context of ethical business conduct and responsible use of technology.
- Introduction to technology governance and regulation;
- Navigating GDPR, EU AI Act, and emerging data regulations;
- Fairness, accountability, and transparency in algorithmic decision-making;
- Surveillance, personalization, and power dynamics in modern business;
- International data flows, jurisdictional challenges, and global tech governance models.
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All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Changes in exam type can be made until the course starts. In addition, unforeseen events or external conditions may call for deviations in teaching and exams.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 70 Grouping: Group/Individual (1 - 3) Duration: 3 Week(s) Exam code: MST 00611 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Written School Exam - digital Exam/hand-in semester: First Semester Weight: 30 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: MST 00612 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Student's own work with learning resources | 90 Hour(s) | |
Group work / Assignments | 20 Hour(s) | |
Digital resources | 30 Hour(s) | |
Feedback activities and counselling | 10 Hour(s) | |
Examination | 20 Hour(s) | |
Teaching | 32 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.
