AI and Venture Capital

Corea, Francesco

doi:10.1007/978-3-030-04468-8_15

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Then, extensive feature engineering is performed to cover a wide range of factors influencing company success. In total, over 400 features are generated, which can be categorized into three macro groups according to [27]: features related to (1) the company, such as location and industry, (2) founder characteristics, including demographic information, education background, and prior work experience, and (3) investment factors, including funding details, investor demographic information, and investment track record. The created features include information related to the sensitive attributes, which are converted into binary features by one-hot encoding for the application of Gradient Reversal learning.…”

Section: Feature Engineeringmentioning

confidence: 99%

Mitigating Discriminatory Biases in Success Prediction Models for Venture Capitals

Te,

Wieland,

Frey

et al. 2023

2023 10th IEEE Swiss Conference on Data Science (SDS)

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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.

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Section: Feature Engineeringmentioning

confidence: 99%