Description:

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.

Learning 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.

Outline:

• Importance of Artificial Intelligence
• Fundamentals of Machine Learning
• Classification and Clustering
• AI Bias and Countermeasures
• Explainable Artificial Intelligence
• Ethical Decision-Making
• Taking Responsibility

Methods of Assessment:

Course grade will be determined through a written exam (100%).