Proseminar Management | AI-Platform Governance: Managerial, Ethical, and Institutional Perspectives

Knowledge

Students are expected to have a basic understanding of management theory, microeconomics, and digital transformation. Familiarity with concepts such as market structures, competition, and innovation systems will facilitate deeper engagement with the material.

A general awareness of AI technologies and data-driven business models is advantageous but not required. Participants should be willing to approach the topic from multiple perspectives — economic, ethical, and institutional — and to work analytically with case studies on platform governance.

The course welcomes students from economics, management, public policy, and technology-related programs who are interested in the strategic, regulatory, and societal dimensions of AI ecosystems.

Description

This seminar explores the governance of AI-based platforms from a management-oriented perspective. At its core lies the question of how managers can assume responsibility for efficiency, fairness, and institutional integrity under the conditions of digital transformation.

Combining economic, ethical, and institutional approaches, the seminar examines how leadership can design governance structures, ensure algorithmic transparency, and maintain organizational accountability within data-driven ecosystems. Students engage with theories of platform economics, institutional governance, and managerial ethics to develop a critical understanding of decision-making in algorithmic environments.

Following the structure of a classic academic seminar, the course includes independent literature review, the formulation of a research question, and the preparation of a seminar paper and presentation supported by peer feedback.

Learning objectives

Upon successful completion, students will be able to:

1. Apply key management and governance theories to AI-based platforms;

2. Analyze ethical and institutional dilemmas in digital decision-making processes;

3. Develop and articulate independent research questions in written and oral form;

4. Critically evaluate the interrelations between technology, organization, and ethics.

E-learning

The course is complemented by digital materials available on the university’s learning platform. These include selected exercises, datasets, and accompanying case studies designed for independent study and deeper engagement with the course content. The digital resources support individual learning progress and the application of theoretical concepts to practical questions.

Preparation

Students are expected to familiarize themselves with the conceptual foundations of management theory, governance, and business ethics, as well as with the economic logic of digital platforms. Prior to the first session, participants should review key literature on:

• the role of management in digital transformation,

• fundamental models of platform economics and multi-sided markets,

• ethical and institutional dimensions of algorithmic decision-making.

Recommended introductory readings include:

• Schulz, W. H., Geilenberg, V., & Kleis, H. (2025). Institutional Framework for Hyper-Cooperation: Dynamics in the Digital Economy. International Journal of Sustainable Development and Planning, 20(1), Article 1. https://doi.org/10.18280/ijsdp.200104

• Geilenberg, V., Schulz, W. H., Mize, J., & Kleis, H. (2024). From self-descriptions (SD) to self-recommendations (SR): Evolving Gaia-X for the future European economy. International Journal of Information Management Data Insights, 4(2), 100249. https://doi.org/10.1016/j.jjimei.2024.100249

• Kleis, H., & Schulz, W. H. (2024). From complexity to cooperation: Solving institutional challenges in digital road projects. Edelweiss Applied Science and Technology, 8(6), 1275–1286. https://doi.org/10.55214/25768484.v8i6.2237

• Jacobides, M. G., Brusoni, S., & Candelon, F. (2021). The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Science, 6(4), 412–435.

Comment

The seminar provides a reflective environment for exploring the managerial, ethical, and institutional challenges of AI-platform governance. It emphasizes the interplay between theory and practice by encouraging students to connect conceptual frameworks from economics, management, and ethics with contemporary developments in digital ecosystems.

Discussions are designed to promote analytical precision, ethical awareness, and interdisciplinary dialogue. Through guided literature work, peer feedback, and independent research, participants develop the ability to critically assess governance mechanisms and propose responsible strategies for managing AI-driven organizations.

Detailed information about the examinations

The assessment consists of two complementary components that reflect the seminar’s scientific and analytical orientation:

1. Seminar Paper (70%)
Each student independently develops a research question related to AI-platform governance, based on the seminar readings and discussions.

• Length: approx. 4,000–5,000 words (excluding references)

• Objective: To apply management and governance theories to a specific empirical or conceptual problem concerning AI-based platforms.

• Evaluation criteria:

  • Theoretical coherence and depth of argument

  • Analytical rigor and methodological transparency

  • Integration of ethical, economic, and institutional perspectives

  • Academic writing quality and correct referencing (APA / Chicago style)

2. Presentation & Peer Feedback (30%)
Students present their research in a seminar presentation (15 minutes + discussion) and act as peer reviewers for one other presentation.

• Goal: To critically discuss theoretical approaches, identify conceptual weaknesses, and provide constructive feedback.

• Evaluation criteria: clarity of presentation, quality of peer feedback, ability to synthesize interdisciplinary perspectives.

Overall Focus:
The combined assessment evaluates each student’s capacity to connect managerial responsibility, ethical reasoning, and institutional integrity in the governance of AI-driven platforms.

Second additional field

Please indicate the language of the course here: English