| Description |
1 online resource (10 pages) |
| 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 |
| Notes |
Mode of access: World Wide Web |
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Copyright © 2019 MIT Sloan Management Review 2019 |
| Issuing Body |
Made available through: Safari, an O'Reilly Media Company |
| Notes |
Online resource; Title from title page (viewed August 21, 2019) |
| Form |
Electronic book
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| Author |
Raveendhran, Roshni, author
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Weingarten, Elizabeth, author
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Barnett, Michaela, author
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Safari, an O'Reilly Media Company
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