How Algorithms Can Diversify the Startup Pool
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,...
| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
MIT Sloan Management Review
2019
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| Edition: | 1st edition |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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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| Item Description: | Mode of access: World Wide Web Made available through: Safari, an O’Reilly Media Company |
| Physical Description: | 10 pages |
| ISBN: | 53863MIT61109 |