Teaching at the Professorship of Cyber Trust
Winter Term 2026/27
Course Instructor: Prof. Jens Grossklags, Ph.D.
Description:
The seminar explores the nascent and growing field of the economics of privacy and cyber security and related security/risk governance aspects. Personal information has become a primary economic good for legitimate companies and is collected for countless purposes. For example, targeted advertisements, personalization and price discrimination are enabled by the automated wholesale accumulation of users’ trails; online and offline. Given this background, the key objective of the seminar is a better understanding of the current and future marketplace for personal information. We will draw on methods from computer science as well as the economic and behavioral sciences to contribute to a rigorous comprehension of the challenges and solution approaches for current privacy and security challenges.
SPECIAL FOCUS TOPIC WINTER 2026/2027: In advance of the winter semester, we will announce 1-3 focus topics for the seminar that orient themselves on current challenges in the area of the economics of security and privacy.
Course objectives:
Seminar meetings will be held in the lecture period during the semester. The presentation of results will follow in the final weeks of the lecture period.
Meetings take place in Garching, online / hybrid participation is not possible.
Students are expected to deliver a concise report and a comprehensive presentation about their findings. The exact timeline will be discussed in the introductory sessions. Collaboration in two-person teams is possible with agreement of the instructor.
Requirement:
No specific knowledge required. General interest in interdisciplinary privacy and security topics highly desirable. The seminar language is English.
Notes:
🔜 Application via http://docmatching.in.tum.de/
- There will be no pre-course meeting!
- Information and materials will be made available via Moodle.
- Regular seminar meeting is planned for Wednesday, 12:00 - 15:00, in room 00.13.036 (CIT building Garching).
- According to the policy of our chair, deregistration from courses is possible until the first regular course meeting by written notice to the instructor. Further, regular attendance and participation in seminar meetings will be compulsory and also be part of the assessment.
- Note for MMT-Students: This course is recognized at TUM SOM as module [IN8031] Informatics Advanced Seminar Courses for Management (Specialization in Technology: Informatics (major) or Catalogue of Elective Modules: Informatics (advanced)). Please double-check with your program manager that this module aligns with your curriculum prior (!) to course registration.
TUM Online: Course Description
Course Instructor: Carmen Löfflad, Ph.D.
SPECIAL FOCUS WINTER 2026/2027
Fairness Perceptions of Automated Decision-Making
SUMMARY
In this seminar, students engage with research on people’s fairness perceptions of automated decision-making (ADM) systems, and their behavioral implications for those affected by ADM systems. All participants work on a shared database with recent literature on fairness perceptions of ADM systems. Participants are first introduced to coding academic articles using a structured framework. Building on this joint, coded database, each team examines a specific topic related to how people evaluate the fairness of ADM systems, and their behavioral implications. The results are presented during the seminar and synthesized in a research report. Each team gives one presentation and hands in one research report.
DETAILED DESCRIPTION
Algorithms are increasingly used to make automated decisions that affect individuals’ lives. For example, algorithms help make decisions in finance, healthcare, hiring, and public services. These systems can influence whether an individual receives a loan, how risks are assessed, or what kind of service a person receives. An increasing number of studies investigates how people evaluate and react to such systems, specifically, whether people consider these systems fair, and how distinct attributes within automated decision-making, for example, transparency, shape people’s fairness evaluations. However, the findings from this research are still spread across many different studies. In this seminar, we bring insights from these studies together in a structured way. The aim of the seminar is to comprehensively study how people perceive the fairness of automated decision-making systems, how specific attributes shape fairness perceptions, and what behavioral implications distinct fairness perceptions have. To do this, we will jointly review existing research articles and organize them into a broader picture.
No prior research experience is required. The seminar introduces participants step by step to academic literature work. Students will learn how to search for literature, read research articles, classify main ideas, and summarize findings in a structured way. The seminar is based on a structured framework which helps us organize the research. For example, some studies focus on fairness implications of distinct procedures, such as transparency or explainability. Other studies focus fairness implications of outcomes, such as whether a decision benefits or disadvantages someone. Using this framework, we will develop a comprehensive and structured overview of current research on fairness perceptions of ADM systems.
SEMINAR PROCEDURE
Stage 1: Literature coding
Each team receives a set of research articles. For each article, the team records key information in a shared database, for example, what topic the article covers, which aspect of fairness it addresses.
Stage 2: Descriptive analysis
Using the shared database, students analyze general patterns in the literature. For example: Which fairness issues are studied most often? In which domains is fairness discussed most frequently? How has the focus of research changed over time?
Stage 3: Interpretation and synthesis
Finally, each team works more closely on one part of the framework, reading relevant studies in more detail and summarizing the main insights. Students should identify common themes, differences between studies, and open questions for future research.
Course objectives:
In this seminar, students will learn:
• how to work with academic literature
• how to organize research findings systematically
• how to summarize and compare studies
• how to develop a broader interpretation from many individual articles
• how to work collaboratively on a shared research project
Deliverables: Students are expected to deliver a concise report (English; 3000-4000 words per student in teams of two people) and a comprehensive presentation (10-15min) about their findings.
Requirement:
No specific knowledge required. Strong interest in interdisciplinary work and in reading, understanding, and interpreting quantitative empirical research articles is desirable.
Important:
🔜 Application via http://docmatching.in.tum.de/
🔜 A virtual pre-course meeting is planned for Friday, July 9, 2026, 11:30 - 12:00h
Zoom Link: https://tum-conf.zoom-x.de/j/9366159409?pwd=dXF2VURzNlI3Nm9lQnNrUEl0ekZKQT09
Meeting-ID: 835 0147 8187
Code: 9rWje0
Information and materials will be made available via Moodle.
Planned Course Timeline:
- Kick-off: 23th of October 2026 from 11:00 am - 1:30 pm -- Seminar overview, introduction, expectations and procedures
- Meeting: 30th of October, 2026 from 11:00 am - 1:30 pm -- Introduction to coding, distribution of coding tasks
- Meeting: 27th of November, 2026 from 11:00 am - 1:30 pm -- Discussion of coding results, deciding further thematic focus for each team
- Meeting: 11th of December, 2026, from 11:00 am - 1:30 pm -- Student presentations of descriptive results, discussion of preliminary findings
- Meeting: 15th of January, 2027, from 11:00 am - 1:30 pm -- Discussion of progress
- Hand-in of Seminar Reports: 22th of January, 2027
- Final Meeting: 29th of January, 2027, from 10:00 am -- 2:00 pm -- Student presentations of seminar reports
⇒ The course meetings will take place in room 01.08.033 (CIT building, Boltzmannstr. 3, Garching)
Note: According to the policy of our chair, deregistration from courses is possible until the first regular course meeting by written notice to the instructor. Further, regular attendance and participation in seminar meetings will be compulsory and also be part of the assessment.
TUM Online: Course Description
Course Instructor: Chiara Ullstein
Description:
The seminar aims to familiarise students with the EU AI Act (artificial intelligence regulation) and explore how participatory AI can help operationalize selected provisions of the EU AI Act. By working with the AI Act, students learn to read a legal text, understand what it means for AI to be in compliance with specific provisions of the AI Act, and familiarize themselves with approaches to participatory AI.
Participatory AI can play a critical role in operationalizing AI regulation by fostering an inclusive framework that draws on diverse stakeholders' collective expertise, values, and perspectives. This approach can be perceived as valuable because AI technologies are not developed or deployed in a vacuum; they affect a broad spectrum of social, economic, and ethical realms. By involving a wide range of participants, including technologists, policymakers, ethicists, business leaders, civil society, laypeople, and others, Participatory AI may help ensure that AI development and AI governance are well-informed and reflect societal values. The exploration of approaches to Participatory AI to meet selected Articles of the EU AI Act is the focus of this seminar.
First, the EU AI Act and Participatory AI will be introduced. Students will learn to understand the structure of the proposal and how to interpret it. As part of the introduction to the AI Act, participants will also learn about the context of the EU AI Act, the legislative processes of the European Union, and the current stage of AI Act enforcement procedures. Participants will present either provisions of the AI Act or a paper on Participatory AI. The presentations serve to develop a foundational understanding of the EU AI Act and Participatory AI.
Then, teams of two students select one article from the EU AI Act and develop a strategy or suggestions on how participatory AI could be a means to ensure or contribute to legal compliance with the article. Teams will first present a proposal in the seminar. Then, student teams develop their work in the form of a seminar paper after receiving feedback. At the end of the seminar, they will give a presentation of their work and hand in a seminar paper.
The goal of the seminar is to make students familiar with AI regulation developed by the EU and to sensitize them for approaches to Participatory AI. For the teamwork, the goal is to bring together students from the Informatics & Mathematics Department and the Governance Department to foster interdisciplinary discussion.
Course objectives:
Understand what the AI Act is about and how it influences the development of AI systems. Become familiar with the analysis, critical reading, and application of legislative text.
Understand what Participatory AI is and how it can be beneficially applied to realize provisions of the EU AI Act.
Deliverables
- Presentation (English; 2x 15 min presentation per person+ 5 min discussion)
- Written Seminar Paper (English; ~3000 words per student)
Requirement:
Interest in AI regulation and/or experience with the development of AI; Interest in approaches to Participatory AI
IMPORTANT:
Participation is reserved for students from the Governance Department (50%) and Informatics & Mathematics Department (50%).
🔜 Application via docmatching.in.tum.de
Pre-course meeting:
Date: July 8, 2026, 1:45 pm
Location (meeting room or link to online session): https://teams.microsoft.com/meet/374394833851535?p=XpwwI0xSMkY2Kktgw6
Meeting ID: 374 394 833 851 535
Passcode: XF6n8aZ3
Kick-off Meeting:
Date: tba
Location (meeting room): tba
Planned course timeline: tba
Location (meeting room): tba
Information and materials will be made available via Moodle
Note: According to the policy of our chair, deregistration from courses is possible until the first regular course meeting by written notice to the instructor. Further, regular attendance and participation in seminar meetings will be compulsory and also be part of the assessment.
TUM Online: Course Description
Course Instructor: Michel Hohendanner
Description:
The seminar aims to familiarize students with participatory approaches to AI ethics and explore how applications making use of gamification and/or educative principles are useful for research and development in AI and sustainable HCI.
AI technologies are not developed or deployed in a vacuum; they affect a broad spectrum of social, economic, and ethical realms. By involving a wide range of participants, including technologists, policymakers, ethicists, business leaders, civil society, laypeople, and others, participation may help ensure that AI development and AI governance are well-informed and reflect societal values.
On the methodological side, a prominent example to gamify moral questions with regards to AI is the “Moral Machine Experiment” (https://www.moralmachine.net/). Here, a platform is provided for gathering people’s perspective on moral decisions made by machine intelligence, such as self-driving cars. The experiment shows moral dilemmas, where a driverless car must choose the lesser of two evils, such as causing an accident involving two passengers or five pedestrians. Users judge which outcome they think is more acceptable. Another example that rather aims at explaining how specific AI systems work (here Facial Processing AI) and their limitations, also to question their use, is the project “How Normal am I” (https://www.hownormalami.eu/).
Building on such prior work, students explore the current AI application landscape focusing on the social and ethical dimensions and impact. In parallel, they explore methodological means to work towards desirable developments including principles of participation for AI in software development. The final goal is to build an application in the sense of the Moral Machine Experiment aimed at different social and ethical issues across the current AI application landscape.
The seminar is divided into two phases:
Phase A: In the first phase, (1) principles of participation in AI, (2) spotlights into social and ethical issues inherent in the current AI application landscape, and (3) methodological examples of how to work towards desirable developments in that regard through means of gamified participation in AI research and development and limitations of these approaches are explored. Participants will present reports or papers in the realm of one of these three areas. The presentations serve to develop a foundational understanding of the problem space this seminar wants to prompt change in and possible tools to foster desirable processes.
Phase B: In the second phase, student teams select one specific social/ethical issue area or type of current AI application that they want to deal with in the course of a practical project. Teams will first develop and present a concept proposal for a web/software application making use of gamification principles to foster or operationalize participatory AI. Then, teams build an interactive/functional prototype of this application.
At the end of the seminar, they will give a presentation/demonstration of their work and, after integrating other student groups’ feedback to their applications, hand in a project report.
Course objectives:
Understand how participation in AI research and development can be beneficially applied making use of gamification and/or educative principles.
Reflect on the capabilities and risks of current AI technologies. Build an application, i.e., as a research/didactic tool, with a focus on communicating and exploring social/ethical issues prompted by current AI applications.
Deliverables
- Presentation of selected articles/ paper (English; 10 min presentation per person+ 5 min discussion)
- web/software application, written project documentation (Group; English; ~12 pages in total), and presentation of group project (English; 10 min presentations + 5 min discussion per student)
Please note, the first presentation will be graded individually. For the group project, the group members will be graded collectively. The group grade will account for the concept, functionality, and usability of the developed application (not the code itself), the written project documentation and the group presentation.
Requirement:
Interest in participatory approaches in AI research and development; Interest in Digital/AI ethics and social impacts of AI;
Requirement for participation are solid programming skills, experiences with web/software development, high commitment to collaborating on a group project, high dedication to self-learning in order to build a functioning tool. The instructor will give feedback on the concept, but not on the implementation.
IMPORTANT:
🔜 Application via docmatching.in.tum.de
Pre-course meeting:
Date: July 8, 2026, 6:00 pm
Location (meeting room or link to online session): https://teams.microsoft.com/meet/325779748194621?p=Bk9MSMhLaijQyODbVI
Meeting ID: 325 779 748 194 621
Passcode: Gf2u4ci3
Kick-off Meeting:
Date: tba
Location (meeting room): tba
Planned course timeline: tba
Location (meeting room): tba
Information and materials will be made available via Moodle
Note: According to the policy of our chair, deregistration from courses is possible until the first regular course meeting by written notice to the instructor. Further, regular attendance and participation in seminar meetings will be compulsory and also be part of the assessment.
TUM Online: Course Description
Weekly group meeting of the Chair of Cyber Trust for members and guests of the chair. The seminar includes research discussions and talks about topics related to the activities of the chair.