Joachim Baumann

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

My main research interests are in the areas of machine learning and NLP, with a focus on algorithmic fairness, algorithmic collective action, and LLM hallucinations.

Currently, I am visiting the MilaNLP Lab, where I am working with Dirk Hovy.

In 2022, I was a Data Science for Socal Good Fellow at Carnegie Mellon University, advised by Rayid Ghani and Kit Rodolfa. In 2023-2024, I interned 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 I was hosted by Celestine Mendler-Dünner and Moritz Hardt. I also organize the Algorithms, Law, and Policy working group at EAAMO Bridges (formerly MD4SG).

I am currently looking for postdoc opportunities starting in 2025.

*equal contribution, see Google Scholar for all publications.

• Baumann J, Mendler-Dünner C. Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists (NeurIPS 2024).
• Baumann J, Sapiezynski P, Heitz C, Hannák A. Fairness in Online Ad Delivery FAccT 2024.
• 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 (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 (EAAMO 2023).
• Baumann J, Castelnovo A, Crupi R, Inverardi N, Regoli D. Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias FAccT 2023.
• Baumann J, Loi M. Fairness and Risk: An Ethical Argument for a Group Fairness Definition Insurers Can Use  (Philosophy & Technology 2023).
• Baumann J, Hannák A, Heitz C. Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency (FAccT 2022).
• Baumann J, Heitz C. Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation (SDS 2022).