Course Descriptions

Safe AI and Organizational Policy Making (master seminar Ufü, SoSe 2025)

Artificial Intelligence (AI) is transforming industries and redefining the dynamics of organizational decision-making. However, its potential risks, ethical concerns, and regulatory challenges necessitate a robust framework for safe and responsible AI deployment. This seminar explores the intersection of AI safety and organizational policy-making in light of novel and existing gonvermental regulations such as the EU AI Act. Our goal is to equip future business leaders with the theoretical and practical tools to navigate this evolving landscape. Students will engage with topics such as ethical AI, governance frameworks, risk assessment, and the role of organizational culture in AI adoption. The seminar will start with an introduction on the safe AI and AI policies, and students will then conduct independent research and condense their findings into seminar theses that address critical issues in the field.

Kick-off (tbc): Wednesday, 09.04.2025, 09:00-11:00

Data Science in Practice (Bachelor)

Conversational AI — Technical Fundamentals und Business Applications (Bachelor)

One of the, arguably, most interesting Natural Language Processing applications to-date is Conversational Artificial Intelligence (AI): it enables users to interact with an AI system in the way they would with other humans – through natural language dialogs. For businesses, this advanced technology offers a plethora of possibilities. In this course, we will thus discuss the technical fundamentals underpinning Conversational AI systems (e.g., machine learning) and afterwards, explore some of the most popular use cases (e.g., customer service chatbots).

Ethics and Modern AI (Master)

In this master course, we will look at the intersection of modern AI and ethical considerations: in particular, we will start by laying out the theoretical fundamentals of both, ethics and machine-learning based AI. Next, we will discuss ethical issues of modern AI using the example of text processing. In particular, we will discuss fairness, inclusion, privacy, and environmental sustainability issues, as well as topics around the issue of dual use. For each issue, we will (a) discuss the technical origin of the issue, (b) the nature of its (negative) impact, and (c) how to mitigate those issues. Finally, we will look at the other side of the coin: applications of modern AI for social good.

Introduction to Data Science (Bachelor)

In the digital age, the computational processing and analysis of data becomes more and more important. This course provides a general overview of theoretical concepts and methods relating to modern data pre-processing and analysis.

Sichere Künstliche Intelligenz für Unternehmen (Bachelor)

Generative AI is one of the hottest topics for businesses to date. However, while the many use cases for Generative AI are being explored more and more (e.g., support for report creation, document analysis, etc.), users of these systems must be safe in their professional and personal use of AI systems. For instance, the AI should not generate offensive content or reply to potentially harmful requests. Thus, AI safety is one of the main research challenges for industry and academia.

In this highly interactive course, we will explore (i) how AI safety topics affect business management, (ii) where the safety issues originate from, and (iii) how we can mitigate these issues. To this end, groups of students will choose from a large pool of topics, read academic literature, and work on actionable recommendations for managers of AI systems. Lectures and student contributions will be complemented with guest lectures from AI safety experts.

The goal of this course is to equip students with the necessary skills to identify and mitigate potential safety issues related to the use of Generative AI in businesses. 

Text Analysis for Interdisciplinary Research (Master)

In the digital age, techniques to automatically process textual content have become ubiquitous. The course „Text Analytics for Interdisciplinary Research“ introduces basic concepts of natural language processing as well as best practices for automatically obtaining knowledge from large collections of texts. 

Data Science in Practice (Bachelor)

Link to course:  DS in Practice

Conversational AI — Technical Fundamentals und Business Applications (Bachelor)

One of the, arguably, most interesting Natural Language Processing applications to-date is Conversational Artificial Intelligence (AI): it enables users to interact with an AI system in the way they would with other humans – through natural language dialogs. For businesses, this advanced technology offers a plethora of possibilities. In this course, we will thus discuss the technical fundamentals underpinning Conversational AI systems (e.g., machine learning) and afterwards, explore some of the most popular use cases (e.g., customer service chatbots).

Link to course: Conversational AI

Ethics and Modern AI (Master)

In this master course, we will look at the intersection of modern AI and ethical considerations: in particular, we will start by laying out the theoretical fundamentals of both, ethics and machine-learning based AI. Next, we will discuss ethical issues of modern AI using the example of text processing. In particular, we will discuss fairness, inclusion, privacy, and environmental sustainability issues, as well as topics around the issue of dual use. For each issue, we will (a) discuss the technical origin of the issue, (b) the nature of its (negative) impact, and (c) how to mitigate those issues. Finally, we will look at the other side of the coin: applications of modern AI for social good.

Link to course: Ethics and Modern AI