How Algorithms Can Diversify the Startup Pool
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How Algorithms Can Diversify the Startup Pool

Data-driven approaches can help venture capital firms limit gender bias and make better, fairer investment decisions.

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Given the lack of data on early-stage enterprises, many venture capitalists rely on their intuition when they decide where to invest. The authors of this article make their case for supporting investment decisions based on algorithms designed to avoid discrimination and enhance ROI. The qualitative methodology and small sample size of the authors’ research might diminish the dependability of the paper’s outcomes. Nonetheless, venture capitalists, financial decision makers and anyone with an interest in diversity in the tech industry will find the analysis pertinent.

Summary

Soft criteria – which are prone to bias – often determine venture capitalists’ investment decisions.

Although the data show that female-run and female-founded companies tend to outperform others, only 2.2% of venture capitalist (VC) funding opportunities go to women-led companies. The reason for this discrepancy is likely due to cognitive bias in venture capitalists’ decision making.

VCs admit to making investment decisions based on gut instinct, perceived market fit and the likability of the founders. However, unlike quantifiable measures, such qualitative criteria are prone to bias.

Algorithms can help decision makers alleviate the impact of cognitive biases.&#...

About the Authors

Morela Hernandez and Roshni Raveendhran teach business administration at the University of Virginia’s Darden School of Business. Elizabeth Weingarten is a senior associate at nonprofit consulting firm Ideas42 and the managing editor at The Behavioral Scientist. Michaela Barnett is a doctoral student at the University of Virginia’s Convergent Behavioral Science Initiative.


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