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https://doi.org/10.21256/zhaw-28508| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Mitigating discriminatory biases in success prediction models for venture capitals |
| Authors: | Te, Yiea-Funk Wieland, Michèle Frey, Martin Grabner, Helmut |
| et. al: | No |
| DOI: | 10.1109/SDS57534.2023.00011 10.21256/zhaw-28508 |
| Proceedings: | 2023 10th IEEE Swiss Conference on Data Science (SDS) |
| Page(s): | 26 |
| Pages to: | 33 |
| Conference details: | 10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023 |
| Issue Date: | 2023 |
| Publisher / Ed. Institution: | IEEE |
| ISBN: | 979-8-3503-3875-1 |
| Language: | English |
| Subjects: | Model fairness; Gradient reversal; Venture capital; Success modeling |
| Subject (DDC): | 006: Special computer methods 658.1: Organization and finance |
| Abstract: | The 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28508 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Published as part of the ZHAW project: | Machine Learning-Aided Startup Investing (MALASI) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Te-etal_Mitigating-discriminatory-biases-in-success-prediction-models-venture-capitals.pdf | Accepted Version | 518.81 kB | Adobe PDF |  View/Open |
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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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