How Algorithms Can Diversify the Startup Pool
Data-driven approaches can help venture capital firms limit gender bias and make better, fairer investment decisions.
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.
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.