Algorithmic Fairness in data-based decision making: Combining ethics and technology
At a glance
- Project leader : Prof. Dr. Christoph Heitz
- Deputy of project leader : Dr. Michele Loi
- Project team : Joachim Baumann
- Project budget : CHF 178'000
- Project status : completed
- Funding partner : Innosuisse (Innovationsprojekt / Projekt Nr. 44692.1 IP-SBM)
- Contact person : Christoph Heitz
Description
We develop a consulting approach for helping companies to create data-based decision algorithms that explicitly consider fairness requirements. This approach is based on a new methodology which integrates an ethical choice methodology with a technical implementation methodology.
Publications
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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.
pp. 7110-7114.
Available from: https://doi.org/10.24963/ijcai.2023/828
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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).
Available from: https://doi.org/10.1007/s13347-023-00624-9
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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.
pp. 1002-1013.
Available from: 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.
pp. 2315-2326.
Available from: 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.
pp. 19-25.
Available from: https://doi.org/10.1109/SDS54800.2022.00011