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Management article
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Reference no. SMR61104
Published by: MIT Sloan School of Management
Originally published in: "MIT Sloan Management Review", 2019
Revision date: 14-Oct-2019
Length: 10 pages
Topics: Digital

Abstract

Biases related to gender and other demographic factors creep into decisions about which projects to fund with venture capital. Data-driven approaches can help tease out those biases and limit their impact. Algorithmic methods identify potential instances of discrimination and increase transparency, making it easier to find and fix problems. Aversion to algorithms can be tempered by letting decision makers retain some subjective control over the data-driven process.

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Abstract

Biases related to gender and other demographic factors creep into decisions about which projects to fund with venture capital. Data-driven approaches can help tease out those biases and limit their impact. Algorithmic methods identify potential instances of discrimination and increase transparency, making it easier to find and fix problems. Aversion to algorithms can be tempered by letting decision makers retain some subjective control over the data-driven process.

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