Social Responsibility in Machine Learning


Semester Summer 
Level Master



The course will be held in English.

Machine Learning models are no longer confined to research but increasingly find themselves deployed in applications that permeate our lives. Examples include:

  • Social media content moderation.
  • Selection strategies for advertisements.
  • Facial recognition software for CCTV cameras.
  • Policing.
  • Everyday household items like soap dispensers.

However, the last years have shown 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 discuss current research on how to reduce bias in machine learning systems, how researchers can contribute to more transparent ML tools, and 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 discuss the topics actively each week.

Recommended pre-requisites

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (Optional)  
  • Computer Vision (Optional)


Prof. Dr. rer. nat. Marius Lindauer
Welfengarten 1
30167 Hannover
Prof. Dr. rer. nat. Marius Lindauer
Welfengarten 1
30167 Hannover


  • The meaning of objectivity in data driven science
  • Case studies and audits of ML tools
  • Algorithmic bias reduction techniques
  • Best practices for fairer ML research
  • Privacy and user rights
  • Environmental impact of ML


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

  • A Citizen's Guide to AI by John Zerilli
  • Trust in Numbers by Theodore M. Porter
  • Race after Technology by Ruha Benjamin
  • Data Feminism by Catherine D'Ignazio and Lauren F. Klein