Joachim Baumann

According to our database, Joachim Baumann authored at least 17 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2024

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists.

Joachim Baumann, Celestine Mendler-Dünner

CoRR, 2024

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance.

Catalina Vajiac, Arun Frey, Joachim Baumann, Abigail Smith, Kasun Amarasinghe, Alice Lai, Kit T. Rodolfa, Rayid Ghani

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Identifying, Mitigating, and Anticipating Bias in Algorithmic Decisions.

Joachim Baumann

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model.

Teresa Scantamburlo, Joachim Baumann, Christoph Heitz

CoRR, 2023

Bias On Demand: Investigating Bias with a Synthetic Data Generator.

Joachim Baumann, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, Daniele Regoli

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias.

Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Closing the Loop: Feedback Loops and Biases in Automated Decision-Making.

Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit.

Corinna Hertweck, Joachim Baumann, Michele Loi, Christoph Heitz

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making.

Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

Fair Machine Learning Through Post-processing: The Case of Predictive Parity.

Joachim Baumann, Anikó Hannák, Christoph Heitz

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

An Open-Source Toolkit to Generate Biased Datasets.

Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems.

Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

2022

Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics.

Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz

CoRR, 2022

A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs.

Corinna Hertweck, Joachim Baumann, Michele Loi, Eleonora Viganò, Christoph Heitz

CoRR, 2022

Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation.

Joachim Baumann, Christoph Heitz

Proceedings of the 9th Swiss Conference on Data Science, 2022

Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency.

Joachim Baumann, Anikó Hannák, Christoph Heitz

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Dealers of Peaches and Lemons: How Can Used Car Dealers Use Trusted Car Data to create value?

Joachim Baumann, Liudmila Zavolokina, Gerhard Schwabe

Proceedings of the 54th Hawaii International Conference on System Sciences, 2021