Algorithmic Fairness in data-based decision making: Combining ethics and technology
Auf einen Blick
- Projektleiter/in : Prof. Dr. Christoph Heitz
- Stellv. Projektleiter/in : Dr. Michele Loi
- Projektteam : Joachim Baumann
- Projektvolumen : CHF 178'000
- Projektstatus : laufend
- Drittmittelgeber : Innosuisse (Innovationsprojekt / Projekt Nr. 44692.1 IP-SBM)
- Projektpartner : Universität Zürich / Digital Society Initiative, Zetamind AG
- Kontaktperson : Christoph Heitz
Beschreibung
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.
Publikationen
-
Baumann, Joachim; Hannák, Anikó; Heitz, Christoph,
2022.
Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency [Paper].
In:
2022 ACM Conference on Fairness, Accountability, and Transparency.
FAccT '22 : 2022 ACM Conference on Fairness, Accountability, and Transparency, hybrid, 21-24 June 2022.
New York, NY:
ACM.
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).
2022 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