| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Bias on demand : investigating bias with a synthetic data generator |
| Autor/-in: | Baumann, Joachim Castelnovo, Alessandro Cosentini, Andrea Crupi, Riccardo Inverardi, Nicole Regoli, Daniele |
| et. al: | No |
| DOI: | 10.24963/ijcai.2023/828 |
| Tagungsband: | Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence |
| Seite(n): | 7110 |
| Seiten bis: | 7114 |
| Angaben zur Konferenz: | 32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, S.A.R, 19-25 August 2023 |
| Erscheinungsdatum: | Aug-2023 |
| Verlag / Hrsg. Institution: | International Joint Conferences on Artificial Intelligence Organization |
| ISBN: | 978-1-956792-03-4 |
| Sprache: | Englisch |
| Schlagwörter: | Digitalisierung; Machine learning; Bias; Vorurteil |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people’s lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29508 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Keine Angabe |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Socially acceptable AI and fairness trade-offs in predictive analytics Algorithmic Fairness in data-based decision making: Combining ethics and technology |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Baumann, J., Castelnovo, A., Cosentini, A., Crupi, R., Inverardi, N., & Regoli, D. (2023). Bias on demand : investigating bias with a synthetic data generator [Conference paper]. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 7110–7114. https://doi.org/10.24963/ijcai.2023/828
Baumann, J. et al. (2023) ‘Bias on demand : investigating bias with a synthetic data generator’, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, pp. 7110–7114. Available at: https://doi.org/10.24963/ijcai.2023/828.
J. Baumann, A. Castelnovo, A. Cosentini, R. Crupi, N. Inverardi, and D. Regoli, “Bias on demand : investigating bias with a synthetic data generator,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Aug. 2023, pp. 7110–7114. doi: 10.24963/ijcai.2023/828.
BAUMANN, Joachim, Alessandro CASTELNOVO, Andrea COSENTINI, Riccardo CRUPI, Nicole INVERARDI und Daniele REGOLI, 2023. Bias on demand : investigating bias with a synthetic data generator. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. Conference paper. International Joint Conferences on Artificial Intelligence Organization. August 2023. S. 7110–7114. ISBN 978-1-956792-03-4
Baumann, Joachim, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, and Daniele Regoli. 2023. “Bias on Demand : Investigating Bias with a Synthetic Data Generator.” Conference paper. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 7110–14. International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2023/828.
Baumann, Joachim, et al. “Bias on Demand : Investigating Bias with a Synthetic Data Generator.” Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2023, pp. 7110–14, https://doi.org/10.24963/ijcai.2023/828.
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