Joachim Baumann
Joachim Baumann
ZHAW
School of Engineering
Forschungsschwerpunkt Business Engineering and Operations Management
Technikumstrasse 81
8400 Winterthur
Arbeit an der ZHAW
Tätigkeit an der ZHAW
Doktorand in Algorithmic Fairness
fair-ai.ch
Aus- und Weiterbildung
Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse
algorithmic fairness, data science for social good
Beruflicher Werdegang
2022: Data Science for Social Good / Data Science Fellow / Carnegie Mellon University
2019-2020: Research Assistant / Teaching Assistant / Tutor, University of Zurich
2018-2019: Management- and Technology Consultant, Campana & Schott Switzerland AG
2014-2015: Internship Application Development, Lifeware SA Zürich
Aus- und Fortbildung
MSc in Informatics, 2021, Universität Zürich
BSc in Informatics, 2019, Universität Zürich
Mitglied in Netzwerken
- Digital Society Initiative
- PhD Programme in Data Science
- MD4SG
- Data Ethics Expert Group
- PhD students in AI Ethics
Projekte
Publikationen
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Baumann, Joachim; Loi, Michele,
2023.
Fairness and risk : an ethical argument for a group fairness definition insurers can use.
Philosophy & Technology.
36(45).
Verfügbar unter: https://doi.org/10.1007/s13347-023-00624-9
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Baumann, Joachim; Castelnovo, Alessandro; Cosentini, Andrea; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele,
2023.
Bias on demand : investigating bias with a synthetic data generator [Paper].
In:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence.
32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, S.A.R, 19-25 August 2023.
International Joint Conferences on Artificial Intelligence Organization.
S. 7110-7114.
Verfügbar unter: https://doi.org/10.24963/ijcai.2023/828
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Baumann, Joachim; Castelnovo, Alessandro; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele,
2023.
Bias on demand : a modelling framework that generates synthetic data with bias [Paper].
In:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.
6th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Chicago, USA, 12-15 June 2023.
Association for Computing Machinery.
S. 1002-1013.
Verfügbar unter: https://doi.org/10.1145/3593013.3594058
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Baumann, Joachim; Hannák, Anikó; Heitz, Christoph,
2022.
Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency [Paper].
In:
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency.
5th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Seoul, Republic of Korea, 21-24 June 2022.
New York:
Association for Computing Machinery.
S. 2315-2326.
Verfügbar unter: https://doi.org/10.1145/3531146.3534645
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Baumann, Joachim; Heitz, Christoph,
2022.
Group fairness in prediction-based decision making : from moral assessment to implementation [Paper].
In:
Proceedings 2022 9th Swiss Conference on Data Science (SDS).
9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022.
IEEE.
S. 19-25.
Verfügbar unter: https://doi.org/10.1109/SDS54800.2022.00011