6 ms·

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

by 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.
    - maxerickson 11y ago
      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 11y ago
        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 11y ago
        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).

by inverba 11y ago

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.
  - maxerickson 11y ago
    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 11y ago
      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 11y ago
        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).