SingleView of Module
Module (6 Credits)
Responsible Artificial Intelligence
- Name in diploma supplement
- Responsible Artificial Intelligence
- Responsible
- Admission criteria
- See exam regulations.
- Workload
- 180 hours of student workload, in detail:
- Attendance: 60 hours
- Preparation, follow up: 60 hours
- Exam preparation: 60 hours
- Duration
- The module takes 1 semester(s).
- Qualification Targets
Students
- will leave the course with an understanding of the fundamentals of machine learning and ethical decision-making.
- acquire the ability to discuss various dimensions of AI systems, evaluate issues related to discrimination and bias through algorithms and data, and understand the concept of explainable AI
- will learn about the key motivators and readiness of organizations to engage in responsible AI, as well as the regulatory environment.
- Relevance
The use of AI can 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. For example, deepfakes could become a means of discrediting, manipulation, and propaganda. This is why AI systems need to be developed responsibly and one needs to gain insights into how to develop AI systems that adhere to these standards.
- Module Exam
Zum Modul erfolgt eine modulbezogene Prüfung in der Gestalt einer Klausur (in der Regel 60-90 Minuten). Vom Dozierenden wird zu Beginn der Veranstaltung festgelegt, ob durch freiwillige Testate in Form von Fallstudien, welche die Vorlesungsinhalte vertiefen sollen und Praxisbeispiele darstellen, bereits im Vorfeld Punkte für die Klausur erworben werden können. Für die Möglichkeit der Anrechnung der Testate muss die Klausur unabhängig vom Ergebnis der Testate mindestens bestanden sein. Ist dies der Fall, so bildet sich die Endnote aus dem Ergebnis der mindestens bestandenen Abschlussprüfung zuzüglich der bereits über die Testate erworbenen Punkte. Die Möglichkeit der Anrechnung der Testate auf die abschließende Prüfungsleistung ist auf maximal 20% der in der abschließenden Prüfung maximal erwerbbaren Punkte beschränkt. Bestandene Testate haben nur Gültigkeit für die Prüfungen, die zu der Veranstaltung im jeweiligen Semester gehören. Es ist unabhängig von der Bearbeitung der freiwilligen Testate möglich, die volle Punktzahl für die modulbezogene Prüfung ausschließlich im Rahmen der abschließenden Klausur zu erreichen.
- Usage in different degree programs
- Elements
Lecture with integrated exercise (6 Credits)
Responsible Artificial Intelligence
- Name in diploma supplement
- Responsible Artificial Intelligence
- Organisational Unit
- Lecturers
- SPW
- 4
- Language
- English
- Cycle
- winter semester
- Participants at most
- no limit
- 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.
- Participants