[inverba]

By most definitions, bias includes the negative prior as well as the refusal. For example:

"Bias is an inclination of temperament or outlook to present or hold a partial perspective, often accompanied by a refusal to consider the possible merits of alternative points of view."

http://en.wikipedia.org/wiki/Bias.

[malisper]

> bias includes the negative prior

How is the prior negative if it is accurate?

[beat]

This is where I rant about "common sense". Common sense is a first approximation of reality. It's actually right the majority of the time. If "the majority of the time" is sufficient for your purposes, it's fine. If it's not, then you're a fool for relying on common sense when you need accuracy.

So basically, you're arguing that "common sense" tells you that women at tech conferences are recruiters or HR. And from a common sense perspective, it may be right. But you didn't say common sense. You said "How is the prior negative if it is accurate?"

By definition, the prior is not going to be accurate for a significant minority (if not a majority) of the women at the conference. And every time you're wrong, you are negatively affecting individuals. Don't want to talk to recruiters? You avoid them. You don't bring them into conversations, or don't assume they can keep up. Your avoidance harms their networking opportunities. You're hurting them.

This, this is why bias matters.

[malisper]

I believe this is the heart of the problem of all of the bias flamewars. I think we can agree that it is reasonable to make statements about the group as long as the statements are true (women are more likely to be recruiters). It becomes much more fuzzy when trying to figure out how to apply that to an individual. That is where I draw the line. When you say an individual is a recruiter because they are female, you are doing something harmful.

[beat]

Right. That's where common sense fails us.

The thing is, this sort of thing is pretty easy to manage in real life - just don't make assumptions, and ask people about themselves. This also falls right in line with the classic advice from "How to Win Friends and Influence People". People like you more because you're interested in them, and you don't make harmful assumptions about them.

Sadly, too many people think "Well, I'm not sexist/racist/homophobic", and make excuses for continuing their pattern of bias rather than really questioning their own behavior and finding better ways to act.

[tomjen3]

>The thing is, this sort of thing is pretty easy to manage in real life - just don't make assumptions, and ask people about themselves.

Assumptions are useful, which is why we use them. Yeah you should ask people about who they are and what they do, but do you really want to spend 15 minutes talking with a recruiter that you could have spent talking with a programmer? No, so you have to avoid the recruiters (lets you be stuck with them) and the best you can do is work based on your assumptions.

If you don't like it, try to replace the word assumptions with Bayesian weighted probability.

[yummyfajitas]

A prior (e.g., P(recruiter|woman at tech event) = g ) is accurate if the actual portion of women at tech events who are recruiters is g.

Secondly, suppose the prior is accurate. Lets take a very simple model, suppose g_woman = 0.25 and g_man = 0.05. Further, suppose networking with a developer has a value of 1 utilon and networking with a recruiter has 0 utilons of value.

If I network in order to maximize utility, based solely on my prior (i.e. ignoring any posterior info), I've added 95 utilons to the world for every 100 people I network with. If I behave irrationally and network with men and women equally, I've added only 85 utilons to the world. I've harmed 47.5 men in order to benefit 37.5 women - on net I've harmed 10 people.

(If posterior information is available, then you can even increase utility beyond 95/100.)

This is why math matters, and why carefully thinking things through rather than spouting incorrect soundbites (as the author does) is important.

[maxerickson]

How do you go from a simple abstract model that is rhetorically convenient to actually guiding concrete behavior?

If you walk into a conference and use that model you aren't using anything very meaningful to guide your behavior, you're using a model that probably isn't very true (I would presume that the modal value of networking is ~0, with the occasional valuable introduction bringing the mean up above that).

[yummyfajitas]

Going from models to reality is basically a process of expanding the model until it accounts for enough that you are confident it will work.

Also, the particular model I use only requires a mean positive utility - even if we take a model like yours, the conclusion is unchanged. Variance simply goes up, but the best option is still not talking to women.

[maxerickson]

How do you measure if it works or not?

I mean, if you only talk to men and then measure where you derived utility, I'm not sure you've properly evaluated the model yet (if you interact with a certain percentage of women at conferences and keep track of all this in order to make sure that your model is working out properly, well then, more power to you).

[beat]

First, morality matters. And you've forcibly ejected morality from your equation.

Second, network effects matter. You're not just creating utilons for yourself, you're creating a system that creates utilons. Even within the small and inaccurate world of your invented model, you're not following through to conclusions.

Third, you're making some very arbitrary assumptions, and ignoring other reasonable assumptions. For starters, do you give everyone you network with equal time? If you encounter a recruiter, do you give them the same amount of effort you would give to an engineer, or do you extract yourself and move on to the next person? The expense of equal input is not nearly as high as you're presenting here, assuming you don't "behave irrationally" and give everyone equal time whether it's effective or not.

Pretending that bias and excuses are intellectual rigor by inserting arbitrary, invented numbers into an imaginary equation is just an appeal to authority fallacy.

[yummyfajitas]

Blfr is right, I'm taking utilitarianism as my morality. Specifically, I believe networking with a developer (regardless of gender) is moral, and networking with a recruiter is useless. What morality do you take?

Secondly, I didn't make any assumption that the utility is all mine. The 1 utilon can be split between both parties in some arbitrary manner, it doesn't change the result.

Third, you are correct that I my constraint may not be #recruiters + #developers = 100. It might be alpha x #recruiters + #developers = 100 for alpha < 1. That doesn't change the optimal course of action - my best bet is always to minimize time I spend with recruiters.

Now if you think my model doesn't work, present a better one. But if you are making a fundamentally moral and non-utilitarian point ("networking with lady developers is intrinsically good no matter how many puppies get killed!!!!!"), make that point and don't waste time on positive claims if the truth of the positive claims is irrelevant anyway.

Also, you seem to wildly misunderstand what an appeal to authority is. An appeal to authority would be "I asked Eliezer Yudkowsky and he said I was right." Writing down a simple mathematical model is not remotely an appeal to authority, that's just careful reasoning.

[beat]

Applying a pretense of mathematical rigor is an appeal to authority - the timeless purity of mathematical truth. there are countless historic examples of false rigor to justify immoral behavior as moral - it's the heart of pseudoscience.

I am flatly making a non-utilitarian argument for the morality of not making assumptions. That doesn't mean, however, that a rigorous application of utilitarian morality would not come to the same conclusions. I've made good arguments that your utilitarian equation is inadequate, and will arrive at false conclusions. You can think about those shortcomings, or argue that they aren't (as you did with your third point here), or you can write my argument off as mushy do-gooding because it's not "utilitarian".

Ignoring my criticism because it's not intrinsically utilitarian would be utilitarian. It would not, however, be rigorous.

Utilitarianism without rigor breaks down, almost inevitably. See the problem?

[tomjen3]

Reasonable proposal here: both of your POVs can trivially be reconciled as follows: developers are marked in some special way, such that they can trivially be separated from non-developers. Perhaps the badge is a different color.

This improves the position of everybody in the network: those who want to talk with recruiters can do so, recruiters don't have to talk with anybody who would only waste their time and there be no reason to assume that women developers are not since gender would be a strictly inferior signal to the (non-discriminary) badge color.

[beat]

See, that's a great idea! The exact sort of thing needed to overcome the bias - a better signal.

[blfr]

Yummyfajitas seems to be using utilitarian morality in his argument, not ejecting it altogether. That's what the concern with the net number of people harmed would suggest anyway.

[beat]

Not really. It looks like utilitarian morality on the surface, but it lacks a rigorous analysis of consequences (as I pointed out on at least two fronts). Utilitarian morality's bootstrap definition requires rigor in order to use it - otherwise, there's a real danger of arriving at an immoral conclusion, which means it's not utilitarian.

Putting a pig in a suit doesn't make it a gentleman.

Which gets back to Occam's Razor. Which is more likely... that this was a failure of insufficient rigor, or that it was using utilitarianism and math to appeal to authority? Given that there were multiple violations of rigor, Occam's Razor suggests that this wasn't utilitarian morality at all, but rather mere defensive rhetoric.

Of course, this doesn't imply intent - the author might not realize that his formal-sounding justification was actually rationalization, because of a failure to understand the underlying moral issue. Which is exactly how bias works in most cases.

Someone put it really well recently, in the context of racism and racist police behavior. They said racism isn't waving a Confederate flag around. Racism is looking for excuses every time the police shoot another unarmed black man. People who don't think of themselves as racist or sexist, who actually find those ideas repulsive, are actively racist and sexist all the time! This is because they don't see the bias in their own behavior.