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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorBaumann, Joachim-
dc.contributor.authorCastelnovo, Alessandro-
dc.contributor.authorCosentini, Andrea-
dc.contributor.authorCrupi, Riccardo-
dc.contributor.authorInverardi, Nicole-
dc.contributor.authorRegoli, Daniele-
dc.date.accessioned2024-01-04T14:56:33Z-
dc.date.available2024-01-04T14:56:33Z-
dc.date.issued2023-08-
dc.identifier.isbn978-1-956792-03-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29508-
dc.description.abstractMachine 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.de_CH
dc.language.isoende_CH
dc.publisherInternational Joint Conferences on Artificial Intelligence Organizationde_CH
dc.rightsNot specifiedde_CH
dc.subjectDigitalisierungde_CH
dc.subjectMachine learningde_CH
dc.subjectBiasde_CH
dc.subjectVorurteilde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleBias on demand : investigating bias with a synthetic data generatorde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.24963/ijcai.2023/828de_CH
zhaw.conference.details32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, S.A.R, 19-25 August 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end7114de_CH
zhaw.pages.start7110de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligencede_CH
zhaw.funding.snf187473de_CH
zhaw.funding.zhawSocially acceptable AI and fairness trade-offs in predictive analyticsde_CH
zhaw.funding.zhawAlgorithmic Fairness in data-based decision making: Combining ethics and technologyde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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