5 ms·

I'm fail to see how step 2 leads to step 3. The fact that there are less woman programmers overall should not have any affect on P(good|woman,programmer).

• crooked-v 7y ago
  2 leads to 3 because in a naively designed system, being a man is a stronger predictor of being a good programmer than being a woman, because there are more men than women in high-ranking and high-paying programmer positions.
  • deleted 7y ago
    [deleted]
  • reroute1 7y ago
    Yes but only in very poorly designed systems. We have those now with AI it's no different. I still see no reason to jump from 2 to 3 other than incompetence in design.
    • loopz 7y ago
      The other poster stated "predictive model". A predictive model could easily identify males to be "more fit" (higher p-value) than females, in aggregate. The programmer tries to evade this bias, by ie. removing gender values in the system. However, the system STILL identify males "more fit", due to correllated values (likes Mancester United/Liverpool). Machine Learning is not much more than advanced statistics, so data biases gets amplified, thus naive application is often a poor fit. Even worse, the experts are at loss on how to make such predictive systems "perform better", without introducing biases on their own, since the data and categorizations themselves may be full of biases. In the real world, incompetence may not be a huge hurdle when selling complex systems. Also, these biases are invisible, until one thinks about them or spots them in the wild.
    • crooked-v 7y ago
      Lots of people think "AI" is a magic bullet that will obviate any need to actually think about this stuff.
  • whatshisface 7y ago
    >a stronger predictor of being a good programmer than being a woman Of being a programmer, not being a good programmer. There are more men than women in programming, but the recruiters want to tell good programmers from bad ones, not programmers from nurses. The data clearly shows that a randomly chosen man is more likely to be a programmer than a randomly chosen woman, but that's irrelevant. The likelihood that a randomly chosen female programmer is good should be about the same as the likelihood that a randomly chosen male programmer is good, and that's what hiring managers care about.
    • crooked-v 7y ago
      And yet this kind of problem is happening over and over and over again in real life, such as the automated risk assessments used in Florida deciding that black people are inherently more likely to be criminal than white people. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm...
• ska 7y ago
  That's a central topic of the article linked, that in practice people are finding it difficult to design and train systems without having this effect.
• bluGill 7y ago
  If you are randomly selecting people from the general population it follows. That is if you just grab the basic info of unemployed and call some in for a programming interview culling females from the list before scheduling the interview appointment will save you a lot of time before you find/hire a good programmer. I can't figure out why you would use such an algorithm though. Most people who want to hire programmers look at their resumes from people who have self-selected as programmers (possibly on a job search forum) and interview them. If you decide there are not enough programmers you might hire people at random ("would you be willing to learn to be a programmer if paid?" "yes" "hired, you start Monday") - there might be factors to exclude some people because they can never be great programmers, but I'm not aware of what such factors might be - gender doesn't seem to be one though.
• ncallaway 7y ago
  > I'm fail to see how step 2 leads to step 3. The fact that there are less woman programmers overall should not have any affect on P(good|woman,programmer). You're correct that there should not be any affect. The problem is that many systems are designed such that there is an affect.
  • theamk 7y ago
    Are there such systems? Every paper I saw use trivial models which are unlikely to be used in real life. Like this article, which checks gender associations for words like “money” - that’s not how one would be using AI for hiring in any practical way!