Fair Machine Learning Systems
Benedikt Höltgen (Ph.D. Student)
As consequential decisions affecting people's lives are increasingly being delegated to Machine Learning systems, society now needs to grapple with questions of algorithmic fairness. While this is still a fairly new area of research, similar questions have long been discussed both informally and mathematically in neighbouring fields. 1 would like to contribute to current debates by connecting insights from different research areas and providing mathematical tools. One possible starting point for this will be models of aggregation in economics.
|Primary Host:||Bob Williamson (University of Tübingen)|
|Exchange Host:||Nuria Oliver (DataPop Alliance, Vodafone Institute & The Spanish Royal Academy of Engineering)|
|PhD Duration:||01 September 2022 - 31 August 2025|
|Exchange Duration:||01 October 2024 - 31 March 2025 - Ongoing|