Dealers of Peaches and Lemons: How Can Used Car Dealers Use Trusted Car Data to create value? - Joachim Baumann 002; Liudmila Zavolokina; Gerhard Schwabe (HICSS, 2021)

Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency. - Joachim Baumann 002; Anikó Hannák; Christoph Heitz (FAccT, 2022)

Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. - Joachim Baumann 002; Christoph Heitz (SDS, 2022)

A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs. - Corinna Hertweck; Joachim Baumann 002; Michele Loi; Eleonora Viganò; Christoph Heitz (CoRR, 2022)

Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics. - Joachim Baumann 002; Corinna Hertweck; Michele Loi; Christoph Heitz (CoRR, 2022)

A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems. - Nicolò Pagan; Joachim Baumann 002; Ezzat Elokda; Giulia De Pasquale; Saverio Bolognani; Anikó Hannák (EAAMO, 2023)

An Open-Source Toolkit to Generate Biased Datasets. - Joachim Baumann 002; Alessandro Castelnovo; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (EWAF, 2023)

Fair Machine Learning Through Post-processing: The Case of Predictive Parity. - Joachim Baumann 002; Anikó Hannák; Christoph Heitz (EWAF, 2023)

Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making. - Joachim Baumann 002; Corinna Hertweck; Michele Loi; Christoph Heitz (EWAF, 2023)

FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit. - Corinna Hertweck; Joachim Baumann 002; Michele Loi; Christoph Heitz (EWAF, 2023)

Closing the Loop: Feedback Loops and Biases in Automated Decision-Making. - Nicolò Pagan; Joachim Baumann 002; Ezzat Elokda; Giulia De Pasquale; Saverio Bolognani; Anikó Hannák (EWAF, 2023)

Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias. - Joachim Baumann 002; Alessandro Castelnovo; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (FAccT, 2023)

Bias On Demand: Investigating Bias with a Synthetic Data Generator. - Joachim Baumann 002; Alessandro Castelnovo; Andrea Cosentini; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (IJCAI, 2023)

Identifying, Mitigating, and Anticipating Bias in Algorithmic Decisions. - Joachim Baumann 002 (AAAI, 2024)

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. - Catalina Vajiac; Arun Frey; Joachim Baumann 002; Abigail Smith; Kasun Amarasinghe; Alice Lai; Kit T. Rodolfa; Rayid Ghani (AAAI, 2024)

Fairness in Online Ad Delivery. - Joachim Baumann 002; Piotr Sapiezynski; Christoph Heitz; Aniko Hannak (FAccT, 2024)

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists. - Joachim Baumann 002; Celestine Mendler-Dünner (NeurIPS, 2024)

On prediction-modelers and decision-makers: why fairness requires more than a fair prediction model. - Teresa Scantamburlo; Joachim Baumann 002; Christoph Heitz (AI Soc., 2025)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)