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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28508
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dc.contributor.authorTe, Yiea-Funk-
dc.contributor.authorWieland, Michèle-
dc.contributor.authorFrey, Martin-
dc.contributor.authorGrabner, Helmut-
dc.date.accessioned2023-08-28T13:55:00Z-
dc.date.available2023-08-28T13:55:00Z-
dc.date.issued2023-
dc.identifier.isbn979-8-3503-3875-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28508-
dc.description.abstractThe fairness of machine learning-based decision support systems has become a critical issue, also in the field of predicting the success of venture capital investment startups. Inappropriate allocation of venture capital, fueled by discriminatory biases, can lead to missed investment opportunities and poor investment decisions. Despite numerous studies that have addressed the prevalence of biases in venture capital allocation and decision support models, few have addressed the importance of incorporating fairness into the modeling process. In this study, we leverage invariant feature representation learning to develop a startup success prediction model using Crunchbase data, while satisfying group fairness. Our results show that discriminatory bias can be significantly reduced with minimal impact on model performance. Additionally, we demonstrate the versatility of our approach by mitigating multiple biases simultaneously. This work highlights the significance of addressing fairness in decisionsupport models to ensure equitable outcomes in venture capital investments.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectModel fairnessde_CH
dc.subjectGradient reversalde_CH
dc.subjectVenture capitalde_CH
dc.subjectSuccess modelingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc658.1: Organisation und Finanzende_CH
dc.titleMitigating discriminatory biases in success prediction models for venture capitalsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/SDS57534.2023.00011de_CH
dc.identifier.doi10.21256/zhaw-28508-
zhaw.conference.details10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end33de_CH
zhaw.pages.start26de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2023 10th IEEE Swiss Conference on Data Science (SDS)de_CH
zhaw.webfeedFinTechde_CH
zhaw.funding.zhawMachine Learning-Aided Startup Investing (MALASI)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Te, Y.-F., Wieland, M., Frey, M., & Grabner, H. (2023). Mitigating discriminatory biases in success prediction models for venture capitals [Conference paper]. 2023 10th IEEE Swiss Conference on Data Science (SDS), 26–33. https://doi.org/10.1109/SDS57534.2023.00011
Te, Y.-F. et al. (2023) ‘Mitigating discriminatory biases in success prediction models for venture capitals’, in 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 26–33. Available at: https://doi.org/10.1109/SDS57534.2023.00011.
Y.-F. Te, M. Wieland, M. Frey, and H. Grabner, “Mitigating discriminatory biases in success prediction models for venture capitals,” in 2023 10th IEEE Swiss Conference on Data Science (SDS), 2023, pp. 26–33. doi: 10.1109/SDS57534.2023.00011.
TE, Yiea-Funk, Michèle WIELAND, Martin FREY und Helmut GRABNER, 2023. Mitigating discriminatory biases in success prediction models for venture capitals. In: 2023 10th IEEE Swiss Conference on Data Science (SDS). Conference paper. IEEE. 2023. S. 26–33. ISBN 979-8-3503-3875-1
Te, Yiea-Funk, Michèle Wieland, Martin Frey, and Helmut Grabner. 2023. “Mitigating Discriminatory Biases in Success Prediction Models for Venture Capitals.” Conference paper. In 2023 10th IEEE Swiss Conference on Data Science (SDS), 26–33. IEEE. https://doi.org/10.1109/SDS57534.2023.00011.
Te, Yiea-Funk, et al. “Mitigating Discriminatory Biases in Success Prediction Models for Venture Capitals.” 2023 10th IEEE Swiss Conference on Data Science (SDS), IEEE, 2023, pp. 26–33, https://doi.org/10.1109/SDS57534.2023.00011.


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