Algorithmic Fairness in data-based decision making: Combining ethics and technology
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 implementationmethodology.
Key Data
Projectlead
Deputy Projectlead
Dr. Michele Loi
Project team
Project partners
Universität Zürich / Digital Society Initiative; Zetamind AG
Project status
completed, 01/2021 - 03/2023
Funding partner
Innovationsprojekt / Projekt Nr. 44692.1 IP-SBM
Project budget
178'000 CHF
Publications
-
Fairness and risk : an ethical argument for a group fairness definition insurers can use
2023 Baumann, Joachim; Loi, Michele
-
Bias on demand : a modelling framework that generates synthetic data with bias
2023 Baumann, Joachim; Castelnovo, Alessandro; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele
-
Bias on demand : investigating bias with a synthetic data generator
2023 Baumann, Joachim; Castelnovo, Alessandro; Cosentini, Andrea; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele
-
Group fairness in prediction-based decision making : from moral assessment to implementation
2022 Baumann, Joachim; Heitz, Christoph
-
Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency
2022 Baumann, Joachim; Hannák, Anikó; Heitz, Christoph
Technikumstrasse 81
8400 Winterthur