Social Responsibility in Machine Learning

Prof. Dr. rer. nat. Marius Lindauer


Machine Learning models are no longer confined to research but are deployed in applications that permeate our lives. Examples include social media content moderation, ad selection strategies, facial recognition software for CCTV and policing as well as everyday household items like soap dispensers. The last years have shown, however, that ML models often fall short of their promised performance in practice because of systematic biases. In this lecture we will examine and critically discuss some of these tools to see how these problems come to be. Then we will move on to discuss current research on how to reduce bias in machine learning systems, how researchers can contribute to more transparent ML tools as well as how to give more agency to data subjects. We will also touch on other areas of life ML systems impact, e.g. their contributions to climate change. Participants need no prior knowledge of research in this area, but should be ready to actively discuss the topics weekly.


We strongly recommend that you know the foundations of machine learning in order to attend the course. You should have attended at least one other course for ML in the past. Being familiar with computer vision is a plus but not necessary.

Topics include:


The full list of literature can be found on the StudentIP course page. Recommended books to accompany the lecture are:


The course, including all discussions, will be in English. We will have weekly sessions for which you will prepare by reading a paper of book excerpt matching the topic. Everyone is then expected to join the discussion around that text. For your final grade, you will submit a written report on a ML system or dataset of your choice, drawing on sources from the lecture to discuss its potential positive and negative impacts on society. Further Details See Stud.IP