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ORCIDJoachim Baumann 0002
Person information
- affiliation: University of Zurich, Switzerland
- affiliation: Zurich University of Applied Sciences, Switzerland
Other persons with the same name
- Joachim Baumann 0001 — University of Stuttgart, Germany
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Conference and Workshop Papers
- 2024
[c14]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: 22393-22400- [c13]Joachim Baumann:
Identifying, Mitigating, and Anticipating Bias in Algorithmic Decisions. AAAI 2024: 23385-23386 - [c12]Joachim Baumann, Piotr Sapiezynski, Christoph Heitz, Aniko Hannak:
Fairness in Online Ad Delivery. FAccT 2024: 1418-1432 - 2023
- [c11]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: 7:1-7:14 - [c10]Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli:
An Open-Source Toolkit to Generate Biased Datasets. EWAF 2023 - [c9]Joachim Baumann, Anikó Hannák, Christoph Heitz:
Fair Machine Learning Through Post-processing: The Case of Predictive Parity. EWAF 2023 - [c8]Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz:
Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making. EWAF 2023 - [c7]Corinna Hertweck, Joachim Baumann, Michele Loi, Christoph Heitz:
FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit. EWAF 2023 - [c6]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 - [c5]Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli:
Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias. FAccT 2023: 1002-1013 - [c4]Joachim Baumann, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, Daniele Regoli:
Bias On Demand: Investigating Bias with a Synthetic Data Generator. IJCAI 2023: 7110-7114 - 2022
- [c3]Joachim Baumann, Anikó Hannák, Christoph Heitz:
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. FAccT 2022: 2315-2326 - [c2]Joachim Baumann, Christoph Heitz:
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. SDS 2022: 19-25 - 2021
- [c1]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: 1-10
Informal and Other Publications
- 2024
- [i8]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 abs/2403.12599 (2024) - [i7]Joachim Baumann, Celestine Mendler-Dünner:
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists. CoRR abs/2404.04269 (2024) - 2023
- [i6]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 abs/2305.06055 (2023) - [i5]Teresa Scantamburlo, Joachim Baumann, Christoph Heitz:
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model. CoRR abs/2310.05598 (2023) - 2022
- [i4]Joachim Baumann, Anikó Hannák, Christoph Heitz:
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. CoRR abs/2206.02237 (2022) - [i3]Corinna Hertweck, Joachim Baumann, Michele Loi, Eleonora Viganò, Christoph Heitz:
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs. CoRR abs/2206.02891 (2022) - [i2]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 abs/2206.02897 (2022) - [i1]Joachim Baumann, Christoph Heitz:
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. CoRR abs/2210.10456 (2022)
Coauthor Index
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