Joachim Baumann

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 21

Top Topics

Utility Maximization

Prediction Model

Decision Making

Top Venues

Publications

Joachim Baumann
Identifying, Mitigating, and Anticipating Bias in Algorithmic Decisions. AAAI (2024)
Catalina Vajiac, Arun Frey, Joachim Baumann, Abigail Smith, Kasun Amarasinghe, Alice Lai, Kit T. Rodolfa, Rayid Ghani
Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. AAAI (2024)
Catalina Vajiac, Arun Frey, Joachim Baumann, Abigail Smith, Kasun Amarasinghe, Alice Lai, Kit T. Rodolfa, Rayid Ghani
Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. CoRR (2024)
Joachim Baumann, Celestine Mendler-Dünner
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists. CoRR (2024)
Joachim Baumann, Piotr Sapiezynski, Christoph Heitz, Aniko Hannak
Fairness in Online Ad Delivery. FAccT (2024)
Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák
Closing the Loop: Feedback Loops and Biases in Automated Decision-Making. EWAF (2023)
Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz
Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making. EWAF (2023)
Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli
An Open-Source Toolkit to Generate Biased Datasets. EWAF (2023)
Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems. CoRR (2023)
Corinna Hertweck, Joachim Baumann, Michele Loi, Christoph Heitz
FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit. EWAF (2023)
Teresa Scantamburlo, Joachim Baumann, Christoph Heitz
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model. CoRR (2023)
Joachim Baumann, Anikó Hannák, Christoph Heitz
Fair Machine Learning Through Post-processing: The Case of Predictive Parity. EWAF (2023)
Joachim Baumann, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, Daniele Regoli
Bias On Demand: Investigating Bias with a Synthetic Data Generator. IJCAI (2023)
Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems. EAAMO (2023)
Joachim Baumann, Christoph Heitz
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. SDS (2022)
Joachim Baumann, Anikó Hannák, Christoph Heitz
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. FAccT (2022)
Joachim Baumann, Anikó Hannák, Christoph Heitz
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. CoRR (2022)
Joachim Baumann, Christoph Heitz
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. CoRR (2022)
Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics. CoRR (2022)
Corinna Hertweck, Joachim Baumann, Michele Loi, Eleonora Viganò, Christoph Heitz
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs. CoRR (2022)
Joachim Baumann, Liudmila Zavolokina, Gerhard Schwabe
Dealers of Peaches and Lemons: How Can Used Car Dealers Use Trusted Car Data to create value? HICSS (2021)