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A Way to Detect Bias

paulgraham.com

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Re: A Way to Detect Bias

#121

Post by devalier »

mattmaroon wrote: post #59
WildUtah wrote: post #20

On a simple mathematical basis, this is false. Consider two groups of candidates for a scholarship, A and B. We want to select all candidates that have an 80% or better chance of graduation. Group A comes from a population where the chance of graduation is distributed uniformly from 0% to 100% and group B is from one where the chance is distributed uniformly from 10% to 90%, with the same average but less variation i…

So in this particular case, assuming that First Round's sample size is significant, it may just be that the female founders who seek them out are just on average better than the male ones? I suppose that if women think that the selection process is biased against them, and most do (and it may be) perhaps the less than excellent ones just don't apply, whereas that isn't true for males?

So in this particular case, assuming that First Round's sample size is significant, it may just be that the female founders who seek them out are just on average better than the male ones? I suppose that if women think that the selection process is biased against them, and most do (and it may be) perhaps the less than excellent ones just don't apply, whereas that isn't true for males?

First, the sample size not significant. Adding back one data point, Uber, which was a real data point that was intentionally removed, likely reverses the effect.

But imagine we had real sample of thousands of companies, and it did show the result.

A typical scenario is that different demographics might connect with First Round via different deal flow channels. For instance, one channel might be longstanding personal connections, another channel might be outreach to companies in the news.

Now imagine female founders are much more likely to be found via outreach rather than personal connections. Perhaps this is due to a negative personal bias -- the VC's are less likely to be chummy with females because of their sex. So they only find female founders when their company is in the news.

It is typical in all businesses that different deal-flow channels have different average returns. So:

* If both channels perform equally well, no bias will be seen in the statistics, even though the VC's are in fact biased.

* If the outbound channel generally performs worse, then women founders in the sample will perform worse than average, even though the VC's are actively biased against them (they are ignoring all the women who would have done well, if only that had personally known them. Sine the VC's never invest in them, their results are not measured). This is the opposite of the statistical relationship that PG claims should exist.

* If the outbound channel generally performs better, then women in the sample will be better than the average.

I should also add that the differences in the channels might be due to a positive bias on the part of the VC -- perhaps they do more aggressive outbound outreach in order to get more female founders in the pipeline. Or the difference might be due to something completely neutral.

The lesson here is that using statistics is a perilous endeavor. If you want to detect something like bias, you cannot use numbers alone, you need to combine any numbers with a deeper understanding of the selection process. There is no way that a third party can run a simple correlation and determine with any degree of certainty that the field is in fact biased or not.

Re: A Way to Detect Bias

#122

Post by gleb »

pg wrote: post #91
WildUtah wrote: post #20

On a simple mathematical basis, this is false. Consider two groups of candidates for a scholarship, A and B. We want to select all candidates that have an 80% or better chance of graduation. Group A comes from a population where the chance of graduation is distributed uniformly from 0% to 100% and group B is from one where the chance is distributed uniformly from 10% to 90%, with the same average but less variation i…

It's true that this test assumes groups of applicants are roughly equal in (distribution of) ability. That is the default assumption in most conversations I've been involved in about bias, and particularly the example I used, but I'll add something making that explicit.

I like the idea, but how do you apply this to power law distribution outcomes and get any statistical significance? I don't know the answer.

E.g. the underlying First Round's analysis likely has no statistical significance. Assuming the power law distribution of outcomes top 5 outcomes will account for 97% of value. So we now have a study with n=5.

To make the point let's apply this to YC's own portfolio. Assuming Dropbox, AirBnb and Stripe represent 75% of its value, we'll learn that YC is incredibly biased against:

  * MIT graduates
  * brother founders
  * founding teams that do not have female founders
  * and especially males named Drew
Hard to believe these conclusions are correct or actionable

Re: A Way to Detect Bias

#123

Post by vog »

iand wrote: post #117
rewqfdsa wrote: post #107

Earlier quoted context omitted.

Favoring fair outcome over having fair rules is against everything I believe. We should not strive for a participation trophy culture.

This well-circulated image shows that making everyone a winner has merit in some circumstances. http://static.themetapicture.com/media/funny-equality-justic... The left hand side is fair rules, the right hand side shows a fair outcome

The link is down (shows a jpeg with a single white pixel), but I guess that one is the same:

http://themetapicture.com/points-of-view/

Re: A Way to Detect Bias

#124

Post by Nimitz14 »

mrwilliamchang wrote: post #104

I think I have a simpler counterexample to disprove pg's hypothesis than any other counterexample I've read in the comments. Suppose our goal is to admit the top 5 applicants with the following performances: A - 30,000 A - 10,000 A - 9,000 B - 7,000 B - 5,000 # Cutoff point below this line A - 4 B - 3 B - 2 Even though admitting the top 5 by score is perfectly fair, the applicants from group A perform better.

I don't see what you're getting at. Group A is better and there are more of them. What's the problem?

Re: A Way to Detect Bias

#125

Post by superuser2 »

lexcorvus wrote: post #112
rewqfdsa wrote: post #107

Earlier quoted context omitted.

Favoring fair outcome over having fair rules is against everything I believe. We should not strive for a participation trophy culture.

Indeed. The problem is that people frequently infer unfair rules from unequal outcomes, without taking into account the possibility of systematic group differences. Alan: I believe in equality of opportunity, not equality of outcome. Bob: How do you know there isn't equality of opportunity? Alan: Well, just look at how unequal the outcomes are! At this point, Bob would be wise to change the subject, because if he pre…

Apply Occam's Razor to these supposed group differences. Which do you think is a more plausible reality?

A. Interviewers prefer candidates who are like themselves, interviewers are mostly white men, therefore most hires are white men.

B. The uterus and melanin both inhibit programming ability, interviewers are perfect judges of programming ability, therefore most hires are white men.

To look at the present (incomplete) evidence and decide that B is the more likely story, is racism/sexism.

Re: A Way to Detect Bias

#126

Post by devalier »

yummyfajitas wrote: post #49
WildUtah wrote: post #20

On a simple mathematical basis, this is false. Consider two groups of candidates for a scholarship, A and B. We want to select all candidates that have an 80% or better chance of graduation. Group A comes from a population where the chance of graduation is distributed uniformly from 0% to 100% and group B is from one where the chance is distributed uniformly from 10% to 90%, with the same average but less variation i…

Graham's intuition is assuming equality of the two distributions. As I noted in a different comment here, you can pretty easily fix Graham's test. Compute min(accepted a) and min(accepted B) instead of the means. In your example, the min of the accepted distributions would both work out to be 80%.

Compute min(accepted a) and min(accepted B) instead of the means.

Dude, your comments are normally smarter than this. Yeah, you can easily fix Grahams's test -- all you need are some numbers that do not exist and that we cannot measure.

We're talking about VC's evaluating founders. That does not, and cannot, get reduced to a numerical score. And even if VC's did use some sort of scoring rubric, then we would still not know if there was unfairness in the way they made the scores, or unfairness in the selection process. It would just be punting the problem down a layer. PG's central claim -- that a third-party can detect the bias/unfairness in the funding process just using math -- is false.

You can only know if the process is biased/unfair if you have deep qualitative understanding of the process.

Re: A Way to Detect Bias

#127

Post by cookiecaper »

srtjstjsj wrote: post #70
cookiecaper wrote: post #60

Earlier quoted context omitted.

I didn't say "just culture"; I said the confluence of social, cultural, and physical factors. Why does "culture" develop? Because people are naturally evil and black-hearted? These things don't happen in a vacuum, they develop organically because they are the best way to support human and tribal propagation and prosperity. Perhaps some things can and should change, but things that are constant across nearly all succe…

Before you take your theory too far, you need to explain why it's OK that your theory implies that black people in America were best suited to be slaves, up until the day they weren't.

Black slavery proves my point. Seen in the context of an experimental social institution, it was a massive failure that barely made it 8 generations before it completely imploded on itself (and took the lives of 650k Americans with it). There's no doubt that it seriously harmed everyone associated with its practice (including in ways you don't usually hear people mention, like decreased ambition and work ethic for everyone, slaves and masters, in slave economies), even mostly-innocent parties who were "guilty by association" like the free states. We're lucky that the US survived black slavery.

Slavery has been tried many times but the gross inequity it inflicts means that no one can operate a stable economy or social system that depends on it.

Re: A Way to Detect Bias

#128

Post by compbio »

pg wrote: post #91
WildUtah wrote: post #20

On a simple mathematical basis, this is false. Consider two groups of candidates for a scholarship, A and B. We want to select all candidates that have an 80% or better chance of graduation. Group A comes from a population where the chance of graduation is distributed uniformly from 0% to 100% and group B is from one where the chance is distributed uniformly from 10% to 90%, with the same average but less variation i…

It's true that this test assumes groups of applicants are roughly equal in (distribution of) ability. That is the default assumption in most conversations I've been involved in about bias, and particularly the example I used, but I'll add something making that explicit.

If feel the addition:

    "C" the applicants you're looking at have roughly 
    equal distribution of ability.
makes the reasoning more tautological/weak.

If we take two dart boards (one for female -, one for male founders) as a visual, where hitting near the bull's eye counts as "startup success".

If we take "C" to be true, then the darts would be thrown at random.

Now we draw a circle around the bull's eye. Anything landing in this circle we fund. If this circle has a smaller radius on the female dartboard, than on the male dartboard, then evidently the smaller female circle will contain more darts closer to the target (better average performance) than the larger radius male circle.

But then we do not even need performance numbers: Smaller radius circles will have less darts in them. Using "C" we only need to know that the male-female accept ratio is not 50%-50% for us to have found a bias.

In short: If you see a roughly equal distribution of ability, and (for simplicity) a roughly equal number of female to male fundraisers, then you should always have a roughly equal distribution of female to male founders in your portfolio, performance be damned.

The technique is still useful for when you do not have these female vs. male accept ratio's, and a VC publishes only success rates, but this information on ratio's is often more public than success rates/estimates.

Re: A Way to Detect Bias

#129

Post by baddox »

55555 wrote: post #93
hibikir wrote: post #85

Earlier quoted context omitted.

The problem here is language and what our actual objectives are. When people complain about bias, they are not really talking about mathematical bias, but about something else: Their idea of fairness. They are talking about discrimination. And when we are discussing that, we can't really think about whether rules are applied fairly or not, but whether the rules produce the outcomes that we want. Let's go for a ludicr…

Great comment. There are two types of fairness, (a) fair rules, and (b) fair outcome.

Which are essentially two of the big branches of normative ethics: deontology and consequentialism.

Re: A Way to Detect Bias

#130

Post by rewqfdsa »

superuser2 wrote: post #125
lexcorvus wrote: post #112

Earlier quoted context omitted.

Indeed. The problem is that people frequently infer unfair rules from unequal outcomes, without taking into account the possibility of systematic group differences. Alan: I believe in equality of opportunity, not equality of outcome. Bob: How do you know there isn't equality of opportunity? Alan: Well, just look at how unequal the outcomes are! At this point, Bob would be wise to change the subject, because if he pre…

Apply Occam's Razor to these supposed group differences. Which do you think is a more plausible reality? A. Interviewers prefer candidates who are like themselves, interviewers are mostly white men, therefore most hires are white men. B. The uterus and melanin both inhibit programming ability, interviewers are perfect judges of programming ability, therefore most hires are white men. To look at the present (incomplet…

Which is more plausible?

a) the action of natural selection, sexual selection, and the hormone environment magically stop at the blood-brain barrier, or

b) there are real group differences between human populations?

We've already eliminated all overt discrimination. If you continue to cry discrimination, you're essentially postulating a giant unconscious conspiracy. I find the idea wildly implausible. It's much simple to just accept that not everyone is equal in aptitude and ability.

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