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A Way to Detect Bias

paulgraham.com

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Re: A Way to Detect Bias

#161

Post by [deleted] »

lexcorvus wrote: post #153
baddox wrote: post #132

Earlier quoted context omitted.

There is a simple explanation for differences in abilities between groups that has nothing to do with their genetics are so-called natural ability: the fact that groups often grow up around other members of their group. Both nature and nurture are largely in common for many groups, so it could easily be either that causes the observed differences in ability.

could easily be either Or both. They're not mutually exclusive. Do Jamaican sprinters excel because they grow up around other sprinters or because they are blessed with natural ability? Yes. Simply put, or != xor .

[deleted]

Re: A Way to Detect Bias

#162

Post by esfandia »

MichaelGG wrote: post #97
esfandia wrote: post #90

Earlier quoted context omitted.

The argument is that First Round Capital must have implictly made it harder for female founders to get funding, since the ones who do perform better. The rational course of action for First Round Capital would be to lower their threshold on female founders (or, conversely, raise the threshold on male founders) until they perform no better or no worse than male founders.

And that's not proven by the evidence. It might be a good thing to look into, but pg's statement that you don't need more info is wrong.

Yeah, I was just rephrasing PG's argument for the OP, not saying whether it was right or wrong. I'm not smart enough to know.

Re: A Way to Detect Bias

#163

Post by kenko »

"What it means for a selection process to be biased against applicants of type x is that it's harder for them to make it through. Which means applicants of type x have to be better to get selected than applicants not of type x. [1] Which means applicants of type x who do make it through the selection process will outperform other successful applicants."

There are many, many reasons that both sentences beginning "which means" are false that someone who is as smart as we're told Graham is should be able to come up with quite easily. It's astonishing that he made this tripe public.

Here's a gimme for each.

Say I'm selecting people to receive a prize; there are ten recipients and they're putatively chosen by [whatever]. But I don't like people with green eyes, so green-eyed candidates had better be pretty pleasing to me. But they can please me in any way, not necessarily in ways relevant to the metric for which the prize is awarded—maybe I also like tall people so a really tall green-eyed person averages out in terms of my predilections. They aren't relevantly better.

For the second, again, the question is "better" at what? Better at getting whatever is involved in getting selected? That doesn't necessarily correlate with outperforming anyone subsequently, especially if it's a matter of startupland. (Remember that New Yorker profile of Marc Andreessen, where Sam Altman basically admitted that he didn't know what he was doing in terms of selecting what to invest in? The flipside of that is being selected by Altman for an investment.)

Re: A Way to Detect Bias

#164

Post by dalek2point3 »

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…

absolutely spot on. Differences in distribution is only one way in which you could disprove pg. There are others. For example, different "treatment effects". If conditional on getting selected, VCs pay more attention or are more useful for women, then that would be another reason that we would get the pattern pg proposes, but is not due to bias at selection.

Re: A Way to Detect Bias

#165

Post by Tloewald »

yummyfajitas wrote: post #33

People, most of whom clearly are not that good at math, are being really harsh on Paul Graham. Graham is mostly right, but slightly incorrect. In particular, suppose group A has the distribution f(x) and B has the distribution g(x). If f(x) and g(x) are shaped significantly differently past the cutoff , then mean(H(x-C)f(x)) and mean(H(x-c)g(x)) might not agree even though there is no bias by construction. (Here H(x)…

There's also the fact that the effect size is huge — 63%. Most real world population differences are not weird enough to generate that large an effect without bias. The bigger questions are how much would the effect size change without removing outliers (Uber as pg mentions but presumably others too) and how significant is the result.

Re: A Way to Detect Bias

#166

Post by jameshart »

There's a flaw right in the assumptions here: "(c) the groups of applicants you're looking at have roughly equal distribution of ability."

Oh. See, the problem is that if an application process is biased, and applicants perceive that bias, then those against whom it is biased will be dissuaded from applying unless they far exceed the required standards. Whereas those towards whom the process is biased will be more likely to apply, even if they are marginally qualified, because they expect to benefit from the bias.

So that means that if you do have a biased process, there's a good chance it doesn't meet criterion c - applicants in the different groups between which its bias discriminates are not equal in ability. So your test might verify a lack of bias, when there is in fact bias present.

You can't verify a lack of bias just by looking at the outcomes of successful applicants - you need to look at the outcomes for unsuccessful applicants too, to determine whether your applicant pools really do meet criterion c. Or you could look at the outcomes for nonapplicants, but that's clearly a much harder problem.

Re: A Way to Detect Bias

#167

Post by gozo »

kiyoto wrote: post #109
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…

What surprised me a bit is that pg decided to use the word "bias" without any clarification, considering his background in computer science and AI. Anyway, I think pg's whole argument is rather moot because the three assumptions that he states are incredibly difficult to measure (Part of the reason why it is very difficult to argue for or against affirmative actions without coming across as "biased").

Unfortunately I think this is a problem with many of his essays. They often present a very specific argument with reservations, which makes the argument very hard to disagree with since you have to argue relevance which requires a lot more insight. It's therefor taken as truth by the readers, even if the original argument don't support their conclusion. In general I think they should be seen as opinion pieces rather essays. I have a hard time seeing many of them being up to e.g. basic university standard.

Re: A Way to Detect Bias

#168

Post by pygy_ »

rewqfdsa wrote: post #120
iand wrote: post #117

Earlier quoted context omitted.

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 baseball game in the image wouldn't be worth if equality of outcome were the rule for baseball team tryouts. The entire game is based on fair competition under the rules pushing participants toward excellence. Inequality of outcome is the entire reason we see baseball played at a high level. When you demand equality of outcome regardless of talent or effort, you're asking for society to stagnate. You're asking fo…

You're beating a straw man. He said in some circumstances.

Re: A Way to Detect Bias

#169

Post by graycat »

graycat wrote: post #101

Okay, PG has an hypothesis test. There's a large literature for that, e.g., E. L. Lehmann, Testing Statistical Hypotheses . E. L. Lehmann, Nonparametrics: Statistical Methods Based on Ranks . Sidney Siegel, Nonparametric Statistics for the Behavioral Sciences . In this case, PG will be more interested in the non-parametric case, i.e., distribution-free where we make no assumptions about probability distributions. We…

Errata:

Replace

Type I Error: We reject the null hypothesis when it is true, e.g., we conclude bias when there is none.

Type II Error: We fail to reject (i.e., we accept) the null hypothesis when it is false.

with

Type I Error: We reject the null hypothesis when it is true; e.g., we conclude bias when there is none.

Type II Error: We fail to reject (i.e., we accept) the null hypothesis when it is false; e.g., we conclude there is no bias when there is.

Replace

For a random number, how about starting with a 32 bit integer, with appropriately long precision arithmetic multiply by 5^15, add 1, take modulo 2^47, and scale as we want?

with

For a random number, how about starting with a 32 bit integer, with appropriately long precision arithmetic multiply by 5^15, add 1, take modulo 2^47, take the resulting integer, scale as we want for stirring our pot, and use that integer as the start of another random number?

Replace

Else First Round looks like the good guys, are "certified statistically fair to women", may get more deal flow from women, and Betty, et al., can be happy that First Round is so nice!

with

Else First Round looks like the good guys, are statistically certified fair to women, may get more deal flow from women, and Betty, et al., can be happy that First Round is so nice!

Replace

Notice that either way Betty, et al., are "happy". That's called "happy women, happy life"! Or, heads, the women win, tails they lose, and in no event is there a huge crowd of angry women in front of First Round's offices with a bonfire of lingerie screaming "bias"!

with

Notice that either way Betty, et al., are "happy". That's called "happy women, happy life"! Or, heads, the women win, tails First Round loses, and in no event is there a huge crowd of angry women in front of First Round's offices with a bonfire of lingerie screaming "bias"!

Replace

So, we want some statistical a hypothesis test that is both multi-dimensional and distribution free.

with

So, we want a statistical hypothesis test that is both multi-dimensional and distribution free.

Replace

We put our money down on the opportunities with highest expected ROI until we have spent all our money.

with

We put our money down on the opportunities with highest expected ROI until we have spent all our money. Done.

Re: A Way to Detect Bias

#170

Post by 174676 »

hibikir wrote: post #85
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…

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…

Sorry but your reasoning does not let pg off the hook. In his article he says that the way to determine bias is by measuring the performance of those that got through. With your excuse, all you have to do is measure the number of people that got through in a particular group relative to the other group.
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