3 ms·

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

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...

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...