“Screaming About Non-Existent Inequality”
There has now been almost four decades of research and one Nobel prize in cognitive bias, judgment and decision-making, and behavioral economics, and yet I still find myself face-to-face with people in the tech industry who insist we’re “screaming about non-existent inequality” (yes, that’s a direct quote from the founder of a tech company).
Bias exists, it’s prevalent in the tech world, and we can do a better job of minimizing it. First, though, we have to agree it’s there. So for those of you still in doubt, here’s an evidence-based discussion of what you are fortunate enough to not see first hand:
1. All humans suffer from some degree of cognitive bias — the tendency to make systematic decisions in certain circumstances based on cognitive factors rather than evidence. In fact, cognitive bias appears to have an evolutionary and biological basis.
2. The more uncertainty and the less evidence available in a given situation, the more one is forced to rely on heuristics to govern an intuitive decision. These heuristics increase cognitive bias.
3. Early stage investors operate and make decisions with rather limited evidence and a very high degree of uncertainty. Ergo, it would be virtually impossible for them to operate without these heuristics (more commonly called intuition or pattern-matching) and the resulting cognitive biases.
4. Representativeness (how similar the stimulus is to standard stored in memory) and availability (how easily one can recall a similar example) are the most commonly used heuristics, and gender is the first thing noticed, coded, and categorized during social interaction. Thus, we should expect a bottlenecking of cognitive bias toward one gender or another (I should note here that race is generally the second trait noticed, coded, and categorized so it shouldn’t be surprising that there’s bias on that metric as well).
5. This effect is compounded by the high prevalence of ingroup bias among virtually all people. Given the overwhelming proportion of investors who are male, the ingroup bias will clearly lean toward men in this situation.
6. Empirical evidence supports the existence of these biases. In one MIT study, identical pitch videos were narrated by men and women, and investors chose the male-narrated videos a whopping 68% of the time.
7. There is logical evidence that market and product based biases exist in addition to these founder-based biases, particularly for consumer-facing start-ups. Investors must believe in both the magnitude of the problem and the usefulness of the solution. If the problem doesn’t impact them personally (i.e. they can’t “feel” or “get” it) and they lack the ability to evaluate the solution, the traction bar will be much higher. Thus, founders serving female-dominant markets are likely to have a harder time raising early capital regardless of how large their market is or how good their initial product is.
I don’t make these points to blame male founders or male VCs or to merely scream about sexism. In fact, the biases I described are all subconscious (i.e. unintentional) and rather innate, even outside the tech community. To pretend they don’t exist, however, is to embody a bias blind spot, and I think we can do better.
An important note: sexism isn’t the biggest problem female founders face. The biggest problems we face are the same ones male founders do — building a solid product, hiring the right people, growing revenues, creating a culture, telling our story, managing investors, etc. — and I spend most of my time stressing over those. That doesn’t mean I can’t point out the reality of the secondary problems, though, and talk about ways to improve things for everyone.
I think most people agree that we want a real meritocracy — both because as a society we value equality of opportunity and because it’s the best way to advance technology and culture for everyone. I have no desire to hold men back or push women forward inappropriately — I simply think we can do a better job of identifying and mitigating biases of all forms — and finding better evidence/predictors of success — so we can achieve that meritocracy.