Lehr-Moodle der Universität Duisburg-Essen

Artificial Intelligence (AI) is widely considered a generative technology that has the potential to have great impact on our society, economy, and ecology. Whether these impacts will be for worse or for better is up for discussion and depends on the actions of individuals, companies, and authorities worldwide towards the 17 UN Sustainable Development Goals.

Throughout the lecture series, students get familiar with concepts and theories that describe and explain AI companies, and learn about the design of Machine Learning-based applications. Do we need AI – or does AI solve our problems? What problems can machine learning effectively solve? What is the current impact of AI technologies on economy, society and ecology? How can we apply AI to a new domain or problem? What role do humans play in designing AI applications?

Building on fundamentals of information systems strategy and enterprise modeling, students reflect the impact of strategy and organizing in AI companies towards their ability to produce sustainable futures. We particularly investigate the generative capacity of data, tools, and (machine learning) models to produce such futures. Among others, we will cover the impact of biases in data and algorithms, explainability of AI applications, as well as accuracy, sovereignty, (inverse) scalability and framing of ML models. Throughout the entire module, we critically reflect impacts of managerial and algorithmic decision-making on sustainability, this includes impacts, for instance, on aspects of health and well-being (SDG 3), gender equality (SDG 5), or climate action (SDG 13).

Course Structure & Schedule 

We will follow the schedule below throughout the course.
All sessions take place from 10:00 a.m. to 2:00 p.m.
Location is R09 R01 H02 .

Monday, April 20th, ²⁰²⁶ :

• (1) Fundamentals of “Artificial Intelligence”
• (2) Fundamentals of “Sustainability”

Friday, April 24th, ²⁰²⁶ :

• (3) AI & ML Techniques (un/supervised) & Numericals

Monday, April 27th, ²⁰²⁶ :

• (4) AI as Socio-technical System
• (5) Presentation of AI Founder

Monday, May 11th, ²⁰²⁶ :

• (6) Neural Networks & Numericals

Friday, May 15th, ²⁰²⁶ :

• (7) "Agentic AI" Workshop Day on n8n.io    

Monday, May 25th, ²⁰²⁶ :

• (8) Building AI Ventures/Managing ML Projects
• (9) AI Agentic in work (session on tools)

Friday, May 29th, ²⁰²⁶ :

• (10) Generative Data
• (11) Sprint Meeting

Friday, June 12th, ²⁰²⁶ :

• (12) Sequential & Generative Modeling & Numericals

Monday, June 19th ^, 2026:

• (13) AI Hackathon Preparation + Actual Day    

Friday, June 26th, ²⁰²⁶ :

• (14) Responsible AI 

Final Presentation Day

• Friday, July 10, 2026 – Room R09R04H02 (Project Presentations)

Final Report Submission

• Friday, July 31, 2026

Trauma n. ‘durch Gewalteinwirkung entstandene Verletzung, Wunde’. Griech. trá͞uma (τραῦμα) ‘Verletzung, Verwundung, Wunde, Schaden, Niederlage’ wird im Sinne von ‘Verletzung, Wunde’ Bestandteil der lat. Wissenschaftssprache der Medizin (18. Jh.).

Wolfgang Pfeifer et al., Etymologisches Wörterbuch des Deutschen (1993),

digitalisierte und von Wolfgang Pfeifer überarbeitete Version im Digitalen Wörterbuch der deutschen Sprache,

abgerufen am 18.07.2025.

Traumakonzepte und -begriffe begegnen uns regelmäßig in unserem Alltag: Mündliche Prüfungen ‚traumatisieren‘ Studierende, manche sind ‚getriggert‘ von Studieninhalten oder Dozierenden, manche finden die ‚Überempfindlichkeit‘ der anderen albern.

In unserem Seminar wollen wir eine gemeinsame, interdisziplinäre Basis schaffen, um über Trauma (und dessen Folgen) sprechen und diskutieren zu können, Darstellungen von Trauma in Literatur, Kultur und Medien zu analysieren und unsere eigenen Meinungen und Standpunkte in gesellschaftlichen Debatten (bspw. zu Triggerwarnungen) zu finden und zu reflektieren.

Unser besonderer Fokus hierbei ist die Diversität/Vielfalt von Trauma im wissenschaftlichen, (pop-)kulturellen und gesellschaftlichen Kontext. Wir wollen uns mit historischen Definitionen von Trauma beschäftigen und damit, wie sich diese unter dem Einfluss vielfältiger(er) Forscher*innen, Aktivist*innen und Praktizierender erweitert, geöffnet und verkompliziert haben. In einem nächsten Schritt analysieren wir dann, inwiefern Trauma zu einer narrativen und stilistischen Strategie und einem Klischee (?) in literarischen, filmischen, medialen (usw.) Erzählungen geworden ist. Abschließend werfen wir einen Blick auf die politischen Diskurse der vergangenen Jahre: Welche Bedeutung hat das Themenfeld Trauma in den aktuellen Diversitätsdebatten? Und inwiefern sind die Inhalte des Seminars auch über dessen Abschluss hinaus sinnvoll und hilfreich im Umgang mit (und der eigenen Positionierung in) diesen Debatten?

This lecture provides a comprehensive introduction to the security, robustness, privacy, and safety of modern Artificial Intelligence (AI) systems, with a particular focus on deep learning. While AI systems are increasingly deployed in safety-critical and security-sensitive domains, they remain vulnerable to a broad spectrum of adversarial, privacy, and misuse-related threats. The course systematically studies these vulnerabilities and presents state-of-the-art defense mechanisms, risk management approaches, and protection techniques.

Students will learn how to analyze AI systems from a cybersecurity perspective across the entire AI lifecycle — from data collection and model training to deployment and post-deployment monitoring. The course integrates theoretical foundations with hands-on exercises, enabling students to implement attacks and defenses in practical machine learning settings.