How Algorithms Can Diversify the Startup Pool

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

When pitching startups, men and women tend to have very different experiences in being evaluated for funding.1 Consider these questions that a venture capital investor might pose to aspiring business owners:

To a male entrepreneur: “Tell us about your vision for this venture.”

To a female entrepreneur: “Tell us about your track record for this type of venture.”

Research shows that men are more likely to receive promotion-focused (risk-loving) questions from investors; for women, prevention-focused (risk-averse) inquiries are the norm.2 Investors also tend to disfavor stereotypically female behaviors, such as being soft-spoken and nurturing (versus bold and assertive), whether those behaviors are exhibited by men or women.3 But even when ventures are pitched in the same way, investors significantly prefer pitches made by men over those made by women.4

One possible explanation for these biases is the so-called cupcake stigma — the perception of women as less serious in their business ventures than the typical male entrepreneur.5 This stigma is reinforced by venture capital funding decisions, which are made mostly by men and thus based primarily on heuristics derived by men. Indeed, less than 10% of decision makers at VC firms are women and 74% of U.S. VC firms have no female investors.6 Despite evidence that suggests companies with female owners and leaders tend to outperform male-owned startups,7 the opportunities for female founders during the past decade have expanded from 1% to only 2.2% of VC funding.8 This scarcity of women in tech is exacerbated by perceptual biases related to gendered social norms and by the persistent structural challenges women face in fields related to science, technology, engineering, and math.

Some VC firms are starting to pay attention to how bias can affect funding decisions.

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