Racism and Meritocracy

And sexist.

I subconsciously attribute certain qualities to men and women, and to various ethnicities. Underneath all of the various common jokes -- "Asians are good at math", etc. -- there is an undercurrent of discussion where other people are admitting their biases, too.

When I get on the phone with technical support and hear a thick East Asian accent, the first thing that happens is I grit my teeth. I feel frustrated that I am having to try to resolve a technical issue with someone that I have to actively concentrate on in order to understand clearly; I feel annoyed in expectation of their overstated politeness; I am immediately resigned to not getting the problem resolved at all, as they are probably going to ask me to troubleshoot a networking problem by rebooting a computer.

These are biases that have been built up over a period of many years of similar experiences, one little bias at a time. And, when the call actually resolves the issue quickly and without any frustration at all, I find myself thinking, "That went better than expected."

I recognize this about myself. So, when I notice these things happening, I consciously moderate my tone, and try to consciously manage my opinions and feelings and statements. But, that doesn't change the fact that the bias is there, affecting my subconscious.

Likewise, for women in tech. I know there are amazing women in technology, and in other male-dominated fields. I'm occasionally fortunate enough to work with some of them, covering a wide range of other demographics. Still, when it comes time to hire again (someday soon, I hope), applications from women will first be met with biases built up one-by-one from a lifetime of experiences: all the various women friends that need help "fixing their computers", or of a past applicant who we hired, presented with an easy starting project and clear instructions -- "no coding necessary, just do some nice-looking Photoshop mockups" -- and who called, the next day, saying that she had cried all night and talked to her mom because she didn't think she could do the work because we used iframes in an ajax-y way.

Or, when I hear about a black entrepreneur. The first thing that bubbles up from my subconscious is, "Wow, they're succeeding in spite of modern black culture." I have to beat that thought down and consciously regard them as an individual.

These are uncomfortable admissions to make. I would be loathe to admit them in more polite company. Terms like "racism" and "sexism" themselves carry strong biases -- that the only forms of "racism" are "white superiority" and the only forms of "sexism" are blatant misogyny and creepy sexual undertones.

But, racism and sexism and other -isms are a part of who we are, a part of our psychology that dates back to our tribal and "us versus them" cultural influences. I think that trying to pretend that they don't exist is only going to prolong their effects.

Knowing that they exist can help us make better decisions.

I was, as were all of the other black computer science students who graduated with me at MIT. Do we get to question your bias now?

  Two normal distributions that are 0.15 standard deviations  
  apart (i.e d=0.15. This is the approximate magnitude of 
  the gender difference in mathematics performance, 
  averaging across all samples.)

So let's take a look at actual data. Here is a significantly more rigorous analysis:

http://www.lagriffedulion.f2s.com/math.htm

This actually uses data from three different tail populations: Female mathematicians in the NAS, Fields Medalists, and Putnam Competition winners. Lo and behold, a simple Gaussian model predicts that small differences in average mathematical ability produce significant sex differences in the tail[1]. And these predictions tally with reality (e.g. the empirical proportion of females in the NAS).

Ms. Oda also does not consider two other crucial aspects, which are:

1. The large difference in spatial ability between men and women:

  http://goo.gl/SGhDw

  On the whole, variation between men and women tends to be 
  smaller than deviations within each sex, but very large 
  differences between the groups do exist–in men’s high 
  level of visual-spatial targeting ability, for one.

  http://goo.gl/ccKyj
                     
  A study by Lubinski and Benbow followed the careers of 
  mathematically precocious youth from age 13 to 23. All 
  were in the top 1% of mathematical ability. At age 23 less 
  than 1% of the girls were pursuing doctorates in 
  mathematics, engineering, or physical science, while 
  almost 8% of the boys were. Equal aptitude not 
  withstanding, girls pursued doctorates in biology at more 
  than twice the rate of boys, and in the humanities at 
  almost three times the rate of boys.

[1] This is of course assuming that distributions remain Gaussian, though it is well known that correspondence to Gaussianity drops off considerably as you move into the tails.

Silicon Valley has many biases, of varying levels of connection to actual merit. One is that it is a very chummy place: who you know is, historically, the first filter applied to you (there is an entire culture around intros) and some folks consider it to be the best available method of approximating merit. This suggests the existence of a strategy which repeatably beats the tar out of the Valley: invest in people before they are known by the powers-that-be, introduce them to the powers-that-be, make out like a bandit. You will probably end up rich enough to take up amusing hobbies like running message boards in your spare time.

It is entirely possible that Silicon Valley has a blind spot with respect to X, where X is a entrepreneurial demographic or a market or a business model or a geographic location or a personality type or a whatever. If X is unconnected to merit, then that suggests the existence of a strategy which will predictably produce crushing. I might even go so far as to say that absence of crushing is fairly persuasive circumstantial evidence about either the existence of the blind spot or about the connection to merit.