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A Way to Detect Bias

paulgraham.com

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Re: A Way to Detect Bias

#131

Post by tedsanders »

devalier wrote: post #126
yummyfajitas wrote: post #49

Earlier quoted context omitted.

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 no…

A charitable interpretation of what he or she said is this: don't evaluate bias by looking at outcomes of the average applicant, look at the outcomes of the borderline applicants. Even if there is no perfect way to define or measure the minimum acceptable applicant, I think it is reasonable to identify whether applicants were borderline or not.

Re: A Way to Detect Bias

#132

Post by baddox »

rewqfdsa wrote: post #114
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…

Right, and unwillingness to consider the possibility of group differences comes from a quasi-religious devotion to the blank slate model of human nature. The way radical egalitarians see it, we're not only equal in dignity, but in potential. That's a pretty view, but it's inconsistent with reality, and radical egalitarians need to come up with increasingly implausible explanations to explain everyday circumstances th…

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.

Re: A Way to Detect Bias

#133

Post by devalier »

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…

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

Serious question: can you at least steel man this point of view rather than making it a ridiculous straw man? If you cannot steel man it, what makes you so sure you really understand the argument?

For bonus points, you can also point out the glaringly obvious complication to this chain of logic: A. Interviewers prefer candidates who are like themselves, interviewers are mostly white men, therefore most hires are white men.

Re: A Way to Detect Bias

#134

Post by tedsanders »

wycats wrote: post #82

The implication of this analysis of http://10years.firstround.com/ is that First Round is biased against founding teams with experience at Amazon, Facebook, Apple, Google, Microsoft or Twitter. Can this be true?

Yes, it can be true. FirstRound could still positively value that experience, but just not be valuing it enough.

Re: A Way to Detect Bias

#135

Post by ericjang »

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…

Your comment makes sense; indeed, fairness in selection process does not imply that people aren't being discriminated against. For instance, the SAT is fair, but denies those with less opportunities a chance to get into top-tier schools. I can get on board with that.

> We can't really think about whether rules are applied fairly or not, but whether the rules produce the outcomes that we want.

This is a more explicit way of phrasing an attitude that I've noticed in my community (a liberal U.S. university). However, I don't think it's obvious that this is the right principle to uphold.

I squirm with discomfort at the idea that we will only support "fairness" and empirical data to the extent that it is applicable to the outcome that we personally desire. This seems to imply that all evaluation metrics are "biased", until we can find a measure that selects equal representation across all demographics, regardless of the size of applicant pool or ability distribution among that pool.

What outcomes, exactly, do we want? More representation of under-represented groups? How does this relate to the goal of maximizing return on the portfolio? What does this mean for people who want a "meritocracy" (if such a thing can exist)?

Thoughts?

Re: A Way to Detect Bias

#136

Post by bmmayer1 »

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

It's never a bad idea to think about how these ideas play out when taken to their logical conclusion.

Kurt Vonnegut on the subject: http://www.tnellen.com/cybereng/harrison.html

Re: A Way to Detect Bias

#137

Post by LukaAl »

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)…

I like your proposal in theory but I doubt it works in practice. I'll try to explain: in practice what First Round has done is measuring post investment performance. I'm pretty sure that if the sample is big enough you will have an "all male" startup and a "with female" startup that failed thus the min being zero in both case. This doesn't mean there's no bias.

The point is that when you decide which company you finance (the source of bias) you make an estimation of future potential. But your test (and PG's one) measure ex-post results. Since there's a lot of uncertainty between the ex-ante and the ex-post measure, your test doesn't work.

Let me put in another way. The measure you are able to perform is not H(x-C)f(x) but a * H(x-C)f(x) + (1-a) * random where random is a random number and a is the weight of f(x) on the final outcome. You are right if a is 1, you and pg are wrong if a is 0 (but it will be a problem for the VCs), for everything in between you have to make assumption on the distribution of random.

Re: A Way to Detect Bias

#138

Post by jdoliner »

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…

I think there's a big problem with this counter example. Which is that you're selecting for one thing and judging whether or not you were biased based on another.

> Now suppose that we select without bias or inaccuracy all the applicants that have an 80% or better chance of graduation.

This is subtly different than selecting for the highest graduation rate possible because it's binary, you want a group with >80% chances not a group with the best chances. Imagine if instead of the distributions you had group A was composed of people with a 100% chance of graduation and B was composed of people with an 80% chance of graduation. Our process does nothing to distinguish between those people because that extra 20% chance of graduation doesn't matter.

This brings me to what I think is the fundamental problem with your criticism, it's not clear to me what it means for a group in your example to over perform. If you select a group with the goal of 80% of them graduating it doesn't make sense to call 90% of them graduating an over performance. That only makes sense if your goal up front is to maximize the graduation rate.

I think if you rerun your example but instead assume an unbiased strategy that selects for the highest graduation rate possible you'll find that pg's essay makes a lot more sense.

Re: A Way to Detect Bias

#139

Post by jonahx »

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…

This is a smart insight, although in fairness the article suggests, in its female founder example, that there was discrimination against women -- that given men and women of equal ability, men were more likely to be chosen.

Condemning that inequality is different from affirming that selection should be altered to produce the outcomes people view as fair. One is saying, "Don't discriminate against Xs." The other is saying, "Not only can't you discriminate against them, you need to ensure that Xs have outcome Y. That is, you may be required to discriminate in their favor."

The latter is a value, and your point about mathematics being irrelevant stands. But the former is a mathematical claim, and pg was making a mathematical claim, so the mathematical argument you replied to is relevant.

Re: A Way to Detect Bias

#140

Post by meowface »

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…

I suppose PG really should have said:

>the groups of applicants you're looking at have exactly equal distribution of ability.

Rather than "roughly equal".

But obviously that makes the whole thing infeasible.

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