Veranstaltungen
Lecture with integrated exercise
Responsible Artificial Intelligence
- Name in diploma supplement
- Responsible Artificial Intelligence
- Organisational Unit
- Lehrstuhl für Wirtschaftsinformatik und Artificial Intelligence im Marketing
- Lecturers
- Prof. Dr. Mario Nadj
- Cycle
- winter semester
- SPW
- 4
- Language
- English
- Participants at most
- no limit
- Participants
Preliminary knowledge
Keines
Abstract
Be it production, customer service, or business innovation, the possibilities of AI are manifold. AI helps to automate repetitive decisions and processes or to detect complex relationships. However, the use of AI can also have unexpected negative consequences that can cause significant damage not only to the reputation and profitability of organizations, but also to workers, individuals, and society as a whole. Prominent examples include deepfakes, the undesirable use of facial recognition, candidate discrimination in personnel selection, or the lack of traceability and control in AI-based business decisions. Organizations therefore need to learn how to responsibly manage human-machine interactions and consider ethical aspects when using AI. However, the study and application of responsible AI is a very young field and requires the pooling of activities from a variety of disciplines to design and apply AI systems in a robust, fair, transparent, and legally acceptable manner. This lecture therefore provides students with a profound overview of the field of responsible AI and introduces fundamental concepts and approaches from a holistic perspective.
Contents
- Importance of Artificial Intelligence
- Fundamentals of Machine Learning
- Classification and Clustering
- AI Bias and Countermeasures
- Explainable Artificial Intelligence
- Ethical Decision-Making
- Taking Responsibility
Literature
- Russell, S. and Norvig, P. (2010) Artificial Intelligence A Modern Approach. 3rd Edition, Prentice-Hall, Upper Saddle River.
- Weitere Literatur wird in der Veranstaltung bekannt gegeben.
Teaching concept
This course follows an interactive approach. Students are expected to actively participate in the classes. Classroom discussions will enable students to critically reflect on the newly acquired knowledge and discuss open questions with the lecturer.
Die Veranstaltung entspricht einem Vorlesungsanteil von 2 SWS und einem Übungsanteil von 2 SWS.