5 ms·

The article is confusing a prior with a bias. They aren't the same thing. A prior is a belief you hold before any evidence is present. A bias is a failure to pr

The article is confusing a prior with a bias. They aren't the same thing. A prior is a belief you hold before any evidence is present. A bias is a failure to properly update your beliefs after obtaining evidence. Believing a woman at a tech conference is a recruiter (before interacting with her) is merely a prior. In my experience it's also a reasonably accurate one. If one were to fail to change that opinion after a woman claims she is a developer, that would be a bias.

inverba 11y ago
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 http://en.wikipedia.org/wiki/Bias.
- malisper 11y ago
  > bias includes the negative prior How is the prior negative if it is accurate?
  - beat 11y ago
    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 11y ago
      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 11y ago
        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 11y ago
        >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 11y ago
      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.
bjt 11y ago
If we were only concerned with making accurate predictions, then all of that would be fine. But the point of the article is less about the accuracy of our predictions, and more about the effects that our priors may have on others. If a female technologist attends a tech conference and gets asked 100 times "are you in HR?", it's very likely that she'll feel unwelcome and discouraged. And her reaction to the questions will have absolutely nothing to do with whether the people doing the asking had a "prior" or a "bias". Creating a welcoming atmosphere for women in technology means that you avoid asking that question despite the fact that the prior may be accurate.
- yummyfajitas 11y ago
  If that's what the article is arguing, it should just say so. I.e., it should say "you should make bad decisions, hold incorrect beliefs, and waste time talking to recruiters in order to make female developers happier". I'm not arguing with the actual message, I'm merely calling for accurate language.
deleted 11y ago
[deleted]
fenomas 11y ago
> Believing a woman at a tech conference is a recruiter (before interacting with her) is merely a prior. The article didn't say anything about believing a woman might be a recruiter, it said stuff about treating her like one. I mean, guys in suits are often recruiters, but that doesn't mean I walk up and ask if they're in HR..

inverba 11y ago

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

malisper 11y ago
> bias includes the negative prior How is the prior negative if it is accurate?
- beat 11y ago
  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 11y ago
    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 11y ago
      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 11y ago
        >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 11y ago
    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.

bjt 11y ago

If we were only concerned with making accurate predictions, then all of that would be fine. But the point of the article is less about the accuracy of our predictions, and more about the effects that our priors may have on others. If a female technologist attends a tech conference and gets asked 100 times "are you in HR?", it's very likely that she'll feel unwelcome and discouraged. And her reaction to the questions will have absolutely nothing to do with whether the people doing the asking had a "prior" or a "bias". Creating a welcoming atmosphere for women in technology means that you avoid asking that question despite the fact that the prior may be accurate.

yummyfajitas 11y ago
If that's what the article is arguing, it should just say so. I.e., it should say "you should make bad decisions, hold incorrect beliefs, and waste time talking to recruiters in order to make female developers happier". I'm not arguing with the actual message, I'm merely calling for accurate language.

deleted 11y ago

[deleted]

fenomas 11y ago

> Believing a woman at a tech conference is a recruiter (before interacting with her) is merely a prior. The article didn't say anything about believing a woman might be a recruiter, it said stuff about treating her like one. I mean, guys in suits are often recruiters, but that doesn't mean I walk up and ask if they're in HR..