10 ms·

I am trying to wrap my head around this. The level of black employees at Google is at 2% and actually dropped since 2016. How fair is it to say from a statistic

I am trying to wrap my head around this. The level of black employees at Google is at 2% and actually dropped since 2016. How fair is it to say from a statistical point of view that if you did not get hired, you lost out to a lesser qualified black person and not someone from a white or asian of equal or more qualifications? I pick on the black statistic specifically because at the end of the day the blow back for these perceived diversity programs lands on this group in particular.

aliston 9y ago
You’re correct that the difference is in the margins. But, at the end of the day, is any exclusion based on race acceptable? From my perspective, it’s hypocritical to champion equal opportunity while systematically having a different hiring process for different races, not to mention illegal.
- spicymaki 9y ago
  That is not the argument I am making. I am asking the question if there is a super majority of white and asian males being hired at these firms (that is present in the Google stats), is it fair to say on an individual basis that the reason you did not get the job was because you were passed over for someone unqualified in an underrepresented group, or is it because said person just did not get hired for some other reason?
  - deleted 9y ago
    [deleted]
sin7 9y ago
If you were to accept the current estimates on IQ, where blacks have an average IQ of 85, Hispanics an average IQ of 90, whites an average IQ of 100 and Asians an average IQ of 105 with a standard deviation of 15 by design, and that Youtube only hires people two standard deviations from the mean, then about 2% of employees would be black, 5% Hispanic, 30% white and 63% Asian. I am quite sure that whites are over represented at the expense of Asians.
- danieltillett 9y ago
  This assumes that there are equal number of people in the hiring pool in each category. There is nothing to suggest that the applicants that apply are drawn at random from the population. Also the absolute numbers of each groups also matter. On this basis we should expect far few Asian and Black employees just on the basis that there are fewer than whites within the USA [0]. 0. This is assuming the employee pool is drawn from the USA population.
  - sin7 9y ago
    Ok. California is 39% Hispanic, 38.8% White, 5.8% Black and 13% Asian. California has 40 Million people. Let's say half have not graduated college yet and are not in the employee pool. That means 8 Million Hispanics, 8 Million whites, 1.16 Million Blacks and 2.6 Million Asians. Taking this new information into account, Asians should be around 37% of the employees, Blacks less than 1%, Hispanics around 9% and Whites 54%. Google's numbers say 61% White, 30% Asian, 2% Black and 3% Hispanic. So, it's both Hispanics and Asians who are under represented.
    - candiodari 9y ago
      Based on those assumptions, which I'll iterate below first, let's get the numbers: 1) hiring based on IQ, cutoff at 2 standard deviations above the global mean (mu = 100, sigma = 15, by design), 2 sigma above that makes 130. You get hired if you're the candidate with the highest IQ, if you satisfy the minimum of an IQ of 130 2) 8/20 hispanic, 8/20 white, 1.16/20 black, 2.6/20 asians (and let's just pretned that sums to 100%). Or: 40% hispanic, 40% white, 5.8% black, 13% asian 3) let's assume 1000 candidates for each position. So each round has 1000 candidates: 400 hispanics, IQ taken from N(90, 15) 400 whites, IQ taken from N(100, 15) 58 black, IQ taken from N(85, 15) 130 asians, IQ taken from N(105, 15) The numbers: 33.42% Asians, 10.85% Hispanic, 0.54% Black, 55.20% White Odds of getting hired under those criteria: 0.25% Asians, 0.02% Hispanics, 0.01% Black, 0.13% White And that's why nobody's going to be happy with expected outcomes. Just imagine the (completely "fair") news headline "Asians 25 TIMES more likely to get hired than blacks in the bay area". import random counts = {'h': 0, 'w': 0, 'b': 0, 'a': 0, None: 0} experiments = 10000 for x in range(experiments): candidates = [] for h in range(400): iq = random.normalvariate(90, 15) candidates.append((iq, 'h')) for w in range(400): iq = random.normalvariate(100, 15) candidates.append((iq, 'w')) for b in range(58): iq = random.normalvariate(85, 15) candidates.append((iq, 'b')) for a in range(130): iq = random.normalvariate(105, 15) candidates.append((iq, 'a')) # filter iq > 130 candidates = [(iq, typ) for (iq, typ) in candidates if iq > 130] if candidates: selected = sorted(candidates, key=lambda (x,y):x)[0][1] else: selected = None counts[selected] += 1 total = sum(counts.values()) print total for k, v in counts.items(): print "%s %2.2f" % (k, 100.0 * v/total) print "odds of hire if hispanic : %2.4f%%" % (100.0*counts['h']/experiments / 400) print "odds of hire if white : %2.4f%%" % (100.0*counts['w']/experiments / 400) print "odds of hire if black : %2.4f%%" % (100.0*counts['b']/experiments / 58) print "odds of hire if asian : %2.4f%%" % (100.0*counts['a']/experiments / 130) print "odds of no hire at all: %2.4f%%" % (100.0*counts[None]/experiments)
deleted 9y ago
[deleted]
danieltillett 9y ago
I think you have put your finger on why these programs are bad for everyone. They really help nobody in the long run and create enormous blowback.
throwaway1584 9y ago
A pretty straightforward explanation is that ⅔ [1] of the tech workers in Silicon Valley are immigrants. Silicon Valley recruits outliers from around the world, and it doesn't make sense to apply US demographics to an industry that recruits from such a pool. If you assume that the Silicon Valley percent of immigrant workers holds for Google you conclude that 6% of the American Googlers are African Americans, since none of the foreigners can be. About 2.4% [2] of computer science degree recipients in the US are African American. [1] https://jointventure.org/images/stories/pdf/index2018.pdf https://jointventure.org/images/stories/pdf/index2018.pdf [2] https://cra.org/crn/2015/09/expanding-the-pipeline-the-state-of-african-americans-in-computer-science-the-need-to-increase-representation/ https://cra.org/crn/2015/09/expanding-the-pipeline-the-state...

aliston 9y ago

You’re correct that the difference is in the margins. But, at the end of the day, is any exclusion based on race acceptable? From my perspective, it’s hypocritical to champion equal opportunity while systematically having a different hiring process for different races, not to mention illegal.

spicymaki 9y ago
That is not the argument I am making. I am asking the question if there is a super majority of white and asian males being hired at these firms (that is present in the Google stats), is it fair to say on an individual basis that the reason you did not get the job was because you were passed over for someone unqualified in an underrepresented group, or is it because said person just did not get hired for some other reason?
- deleted 9y ago
  [deleted]

sin7 9y ago

If you were to accept the current estimates on IQ, where blacks have an average IQ of 85, Hispanics an average IQ of 90, whites an average IQ of 100 and Asians an average IQ of 105 with a standard deviation of 15 by design, and that Youtube only hires people two standard deviations from the mean, then about 2% of employees would be black, 5% Hispanic, 30% white and 63% Asian. I am quite sure that whites are over represented at the expense of Asians.

danieltillett 9y ago
This assumes that there are equal number of people in the hiring pool in each category. There is nothing to suggest that the applicants that apply are drawn at random from the population. Also the absolute numbers of each groups also matter. On this basis we should expect far few Asian and Black employees just on the basis that there are fewer than whites within the USA [0]. 0. This is assuming the employee pool is drawn from the USA population.
- sin7 9y ago
  Ok. California is 39% Hispanic, 38.8% White, 5.8% Black and 13% Asian. California has 40 Million people. Let's say half have not graduated college yet and are not in the employee pool. That means 8 Million Hispanics, 8 Million whites, 1.16 Million Blacks and 2.6 Million Asians. Taking this new information into account, Asians should be around 37% of the employees, Blacks less than 1%, Hispanics around 9% and Whites 54%. Google's numbers say 61% White, 30% Asian, 2% Black and 3% Hispanic. So, it's both Hispanics and Asians who are under represented.
  - candiodari 9y ago
    Based on those assumptions, which I'll iterate below first, let's get the numbers: 1) hiring based on IQ, cutoff at 2 standard deviations above the global mean (mu = 100, sigma = 15, by design), 2 sigma above that makes 130. You get hired if you're the candidate with the highest IQ, if you satisfy the minimum of an IQ of 130 2) 8/20 hispanic, 8/20 white, 1.16/20 black, 2.6/20 asians (and let's just pretned that sums to 100%). Or: 40% hispanic, 40% white, 5.8% black, 13% asian 3) let's assume 1000 candidates for each position. So each round has 1000 candidates: 400 hispanics, IQ taken from N(90, 15) 400 whites, IQ taken from N(100, 15) 58 black, IQ taken from N(85, 15) 130 asians, IQ taken from N(105, 15) The numbers: 33.42% Asians, 10.85% Hispanic, 0.54% Black, 55.20% White Odds of getting hired under those criteria: 0.25% Asians, 0.02% Hispanics, 0.01% Black, 0.13% White And that's why nobody's going to be happy with expected outcomes. Just imagine the (completely "fair") news headline "Asians 25 TIMES more likely to get hired than blacks in the bay area". import random counts = {'h': 0, 'w': 0, 'b': 0, 'a': 0, None: 0} experiments = 10000 for x in range(experiments): candidates = [] for h in range(400): iq = random.normalvariate(90, 15) candidates.append((iq, 'h')) for w in range(400): iq = random.normalvariate(100, 15) candidates.append((iq, 'w')) for b in range(58): iq = random.normalvariate(85, 15) candidates.append((iq, 'b')) for a in range(130): iq = random.normalvariate(105, 15) candidates.append((iq, 'a')) # filter iq > 130 candidates = [(iq, typ) for (iq, typ) in candidates if iq > 130] if candidates: selected = sorted(candidates, key=lambda (x,y):x)[0][1] else: selected = None counts[selected] += 1 total = sum(counts.values()) print total for k, v in counts.items(): print "%s %2.2f" % (k, 100.0 * v/total) print "odds of hire if hispanic : %2.4f%%" % (100.0*counts['h']/experiments / 400) print "odds of hire if white : %2.4f%%" % (100.0*counts['w']/experiments / 400) print "odds of hire if black : %2.4f%%" % (100.0*counts['b']/experiments / 58) print "odds of hire if asian : %2.4f%%" % (100.0*counts['a']/experiments / 130) print "odds of no hire at all: %2.4f%%" % (100.0*counts[None]/experiments)

deleted 9y ago

[deleted]

danieltillett 9y ago

I think you have put your finger on why these programs are bad for everyone. They really help nobody in the long run and create enormous blowback.

throwaway1584 9y ago

A pretty straightforward explanation is that ⅔ [1] of the tech workers in Silicon Valley are immigrants. Silicon Valley recruits outliers from around the world, and it doesn't make sense to apply US demographics to an industry that recruits from such a pool. If you assume that the Silicon Valley percent of immigrant workers holds for Google you conclude that 6% of the American Googlers are African Americans, since none of the foreigners can be. About 2.4% [2] of computer science degree recipients in the US are African American. [1] https://jointventure.org/images/stories/pdf/index2018.pdf https://jointventure.org/images/stories/pdf/index2018.pdf [2] https://cra.org/crn/2015/09/expanding-the-pipeline-the-state-of-african-americans-in-computer-science-the-need-to-increase-representation/ https://cra.org/crn/2015/09/expanding-the-pipeline-the-state...