Joachim Baumann

Joachim joined the Social Computing Group in January 2021 as a PhD student, co-supervised by Anikó Hannák and Christoph Heitz.

Joachim's main research interests are in the areas of machine learning and data science for social good, with a focus on algorithmic fairness and feedback loops in automated decision-making, as well as algorithmic collective action.

Joachim received both BSc and MSc in Informatics from the University of Zurich with minors in Economics and Data Science. During that time, he also spent one semester at Universitat Pompeu Fabra Barcelona and one semester at Universidad de Guadalajara. Before starting his PhD, he interned as an application developer (2014-2015) and worked in management and technology consulting (2018-2019).

In 2022, Joachim was a Data Science for Socal Good Fellow at Carnegie Mellon University.

Currently, Joachim interns at the Max Planck Institute for Intelligent Systems (Social Foundations of Computation Department) in Tübingen (affiliated with the Algorithms and Society Group at the ELLIS Institute Tübingen) where he is working with Celestine Mendler-Dünner and Moritz Hardt.

*equal contribution

• Baumann J, Sapiezynski P, Heitz C, Hannák A. Fairness in Online Ad Delivery. Forthcoming in Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT'2024).
• Baumann J, Mendler-Dünner C. Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists.
• Vajiac C*, Frey A*, Baumann J*, Smith A*, Amarasinghe K, Lai A, Rodolfa K, Ghani R. Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. In the 38th Annual AAAI Conference on Artificial Intelligence (AAAI'2024).
• Scantamburlo T,* Baumann J*, Heitz C*. On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model. AI & Society, 2024.
• Pagan N, Baumann J, Elokda E, De Pasquale G, Bolognani S, Hannák A.  A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems. In Proceedings of the 2023 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'23).
• Baumann J, Castelnovo A, Crupi R, Inverardi N, Regoli D. Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT'2023), pp. 1002-1013.
• Baumann J, Loi M. Fairness and Risk: An Ethical Argument for a Group Fairness Definition Insurers Can Use.  Philosophy & Technology 36, 45, 2023.
• Baumann J, Hannák A, Heitz C. Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT'2022), pp. 2315-2326.
• Baumann J, Heitz C. Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. 2022 9th Swiss Conference on Data Science (SDS'2022), pp. 19-25.

See Google Scholar for all publications.