Automated Decision-Making, AI, and Robots

Project Description

In this research stream we study the social impacts of algorithmic decision-making in various domains and develop methodology to promote fairness and reliability in AI technologies. We study how biases can emerge along the machine learning (ML) pipeline, with focal points on the quality of training data, reliability and transparency in model development processes, and fairness implications of prediction-based decisions in social contexts. We further assess how ML methodology can be adopted across scientific disciplines and how AI research can benefit from social scientific and survey research perspectives.

Contact Person

Prof. Dr. Christoph Kern