Machine Learning and Society (6 ECTS)

What it is about

You engage with the societal, ethical, and technical impacts of Machine Learning. In the end you will be able to critically evaluate ML systems, recognise potential biases and harms, practically apply methods such as auditing and red teaming, and assess the importance of alignment for large language models.

What you will be able to do

• Understand complex interactions between ML and society
• Recognise potential biases and algorithmic harms
• Apply methods of auditing and red teaming
• Assess approaches to alignment of LLMs
• Know formal fairness definitions and assess their trade-offs
• Conceptualise, plan and carry out projects in teams
• Communicate results understandably to different stakeholders

What the module consists of

• VorlesungIntroduction to central concepts of Machine Learning and society; short exercises and questions to ensure understanding
• SeminarGroup work for practical application of concepts; addressing a research question and creating a project

Teaching method

• Vorlesung mit Folien und PräsentationenConveying the central concepts and exemplary cases
• Interaktive Übungen in der VorlesungChecking and consolidating understanding through brief tasks
• Seminar mit GruppenprojektenPractical application of learned methods and teamwork
• Abschließende Präsentation und schriftlicher BerichtDemonstrating the ability to communicate results orally and in writing