This research lab will examine critical issues, current debates and key concepts related to artificially intelligent technologies, and their potential to transform governance processes and society. The course will be divided into project teams working on different topics centred around a real-world case study. Each team will meet regularly with the supervisor to design and execute the project, to discuss progress, write up results and present the final output.

(i) Measuring AI Fairness (contact: Alexander Schiller): Machine learning models can produce unfair outcomes that systematically disadvantage underprivileged groups. With the increasing prevalence of machine learning-based information systems and their integration into high-stakes decision-making processes, it has thus become crucial to consider the fairness of these systems. There is a substantial body of literature on outcome-based approaches for determining fairness, but assessing the underlying decision logic of the machine learning models — i.e., procedural fairness — remains underexplored. This research project focuses on the development and evaluation of a novel approach for measuring procedural fairness. Drawing on related literature and recent advances in explainable AI, the aim is to implement own methodological ideas in Python, apply the approach to real-world datasets containing fairness-critical data in domains such as finance or healthcare, and discuss the findings.

(ii) AI, Language, and Bureaucracy (contact: Shefali Virkar): The benefits of attracting and retaining international talent for universities are widely acknowledged. However, international researchers, much like their student counterparts, face several unique challenges when opting to move abroad for work – including linguistic and cultural barriers. On the one hand, international researchers need to regularly interface with public administration to process visas and residence permits, validate institutional sponsorship, and navigate life events. On the other, language and culture also pervades their experiences in the classroom, whether they have to teach complex material in a local language that is ‘foreign’ to them, or if they need to acquire basic language skills in order to adjust to local students, colleagues, and university bureaucracy. The aim of this project is to explore how international researchers use artificial intelligence-based technologies to bridge existing gaps in language and culture, especially when faced with unfamiliar or linguistically complex bureaucratic procedures.

(iii) Generative Artificial Intelligence and Education (contact: Shefali Virkar): The rapid development of Generative AI tools, and their proliferation within universities has given rise to the phenomenon of Shadow AI, broadly defined as the unsanctioned use of AI tools to support research, teaching and administrative tasks outside existing governance frameworks. Be it the use of these tools to deliberately bypass institutional controls or standards, the desire to engage with the latest tools and technologies, or simple ignorance underpinning such behaviours, a key problem remains: many universities already have AI policies but these might be outdated or unclear, are not widely known or are disregarded by faculty, or can only be enforced in a limited manner. The aim of this research project is to explore the notion of Shadow AI as a socio-technical governance failure, and to critically examine faculty behaviour within these ‘governance drift zones’, where formal policies about Generative AI use exist but are not followed or implemented in practice – with implications for academic excellence and better governance of IT systems.

Upon completion of the course, students are able to

- Critically evaluate a research question in the broad topic of Digital Economy from the view of (micro)economics and information systems
- Plan a research project to answer such a research question
- Perform a structured literature search on a given topic
- Design an experiment or empirical study for a specific research question
- Identify appropriate analysis methods
- Conduct appropriate statistical analyses for said data
- Interpret the results of said analyses and evaluate them critically
- Write a research paper according to current academic standards from the relevant disciplines describing the research project and its outcomes

Attendance is mandatory (at least 80% of all units). Please notify the lecturers about your absence via e-mail before the unit.

Students conduct an interdisciplinary research project spanning all stages: from defining a research question, doing a literature research, to stating hypotheses, implementing an experimental or empirical study, analysing the experimental or empirical data and interpreting and critically reflecting the findings. Besides acquiring methodological knowledge, students gain practical experience in planning and carrying out a research project and also take the perspective of a project manager. Students work in groups and are coached regularly by the two lecturers.

1. Research project plan incl. tasks, responsibilities and milestones (15 %)
2. Intermediate result report incl. update of research project plan, draft of research paper (15 %) incl. critical reflection
3. Peer review (10 %)
4. Final report (40 %) incl. research paper and project work („lessons learned“), Discussion of implications for industry, technical report, project result poster; incl. critical reflection
5. Final presentation (20 %)

Grading scale:

90% to 100% Excellent (1)

80% to <90% Good (2)

70% to <80% Satisfactory (3)

60% to <70% Sufficient (4)

<60% Fail (5)

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