Joachim Baumann

Netzwerk

Mitglied in Netzwerken

• MD4SG
• PhD Programme in Data Science
• Digital Society Initiative
• Data Ethics Expert Group
• PhD students in AI Ethics

ORCID digital identifier

ORCID ID: 0000-0003-2019-4829

Projekte

• Algorithmic Fairness in data-based decision making: Combining ethics and technology / Teammitglied / abgeschlossen

Publikationen

• Scantamburlo, Teresa; Baumann, Joachim; Heitz, Christoph,

  2024.

  On prediction-modelers and decision-makers : why fairness requires more than a fair prediction model.

  AI & Society.

  Verfügbar unter: https://doi.org/10.1007/s00146-024-01886-3

• 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

• Baumann, Joachim; Sapiezynski, Piotr; Heitz, Christoph; Hannák, Anikó,

  2024.

  Fairness in online ad delivery [Paper].

  In:

  Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency.

  7th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Rio de Janeiro, Brazil, 3-6 June 2024.

  Association for Computing Machinery.

  S. 1418-1432.

  Verfügbar unter: https://doi.org/10.1145/3630106.3658980

• 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

• 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

• 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

• 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

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