Causal and Fair Machine Learning

This group focuses on methodological and applied research in the context of causal questions in machine learning and on fairness in machine learning. Topics include

Our research group holds a regular journal club focused on the latest advancements in causality and fairness as published in machine learning conferences and journals. This journal club is open for guests; please contact Ludwig Bothmann if you are interested in joining.

Members

Name       Position
Dr. Ludwig Bothmann       Lead
Dr. Matthias Aßenmacher       PostDoc
Philip Boustani       PhD Student
Simon Rittel       PhD Student
Lisa Wimmer       PhD Student
Helena Veit       BA Student

Alumni

Name       Position
Dr. Gunnar König       University of Tübingen
Dr. Susanne Dandl       University of Zurich

Publications

  1. Bothmann L, Peters K (2024) Fairness als Qualitätskriterium im Maschinellen Lernen – Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln. AStA Wirtschafts- und Sozialstatistisches Archiv.
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  2. Bothmann L, Peters K (2024) Fairness von KI – ein Brückenschlag zwischen Philosophie und Maschinellem Lernen. In: In: Rathgeber B , In: Maier M (eds) Grenzen Künstlicher Intelligenz,
  3. Ronval B, Nijssen S, Bothmann L (2024) Can generative AI-based data balancing mitigate unfairness issues in Machine Learning? EWAF’24: European Workshop on Algorithmic Fairness,
  4. Weerts H, Pfisterer F, Feurer M, Eggensperger K, Bergman E, Awad N, Vanschoren J, Pechenizkiy M, Bischl B, Hutter F (2024) Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. Journal of Artificial Intelligence Research 79, 639–677.
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  5. Dandl S, Haslinger C, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2024) What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? The Annals of Applied Statistics 18, 506–528.
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  6. Bothmann L, Peters K, Bischl B (2024) What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds. arXiv:2205.09622 [cs, stat].
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  7. Dandl S, Bender A, Hothorn T (2024) Heterogeneous treatment effect estimation for observational data using model-based forests. Statistical Methods in Medical Research 33, 392–413.
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  8. Bothmann L, Dandl S, Schomaker M (2023) Causal Fair Machine Learning via Rank-Preserving Interventional Distributions Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), CEUR Workshop Proceedings.
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  9. Öztürk IT, Nedelchev R, Heumann C, Garces Arias E, Roger M, Bischl B, Aßenmacher M (2023) How Different Is Stereotypical Bias Across Languages? 3rd Workshop on Bias and Fairness in AI (co-located with ECML-PKDD 2023),
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  10. König G, Freiesleben T, Grosse-Wentrup M (2023) Improvement-focused Causal Recourse (ICR) 37th AAAI Conference,
  11. Luther C, König G, Grosse-Wentrup M (2023) Efficient SAGE Estimation via Causal Structure Learning AISTATS,
  12. Urchs S, Thurner V, Aßenmacher M, Heumann C, Thiemichen S (2023) How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses 1st Workshop on Biased Data in Conversational Agents (co-located with ECML-PKDD 2023),
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  13. Bothmann L (2022) Künstliche Intelligenz in der Strafverfolgung. In: In: Peters K (ed) Cyberkriminalität, LMU Munich, Munich.
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  14. Dandl S, Pfisterer F, Bischl B (2022) Multi-Objective Counterfactual Fairness Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 328–331. Association for Computing Machinery, New York, NY, USA.
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  15. König G, Grosse-Wentrup M (2019) A Causal Perspective on Challenges for AI in Precision Medicine.
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Contact

Feel free to contact us if you are looking for collaborations:

ludwig.bothmann [at] stat.uni-muenchen.de