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Unequal outcome != bias. Most SJWs don‘t seem to understand this. Of course it can be an indicator that there may be bias, but it is FAR from a proof thereof.

Unequal outcome != bias. Most SJWs don‘t seem to understand this. Of course it can be an indicator that there may be bias, but it is FAR from a proof thereof. I can easily write you a completely unbiased “model” predicting who comitted crimes: func probability_of_comitting_crime(): return 1/human_population This is 100% unbiased and treats everybody the same. It’s also 100% useless...

nkozyra 8y ago
The problem is that people view pure statistical based crime probability as "unbiased" because... math. It's not! Most bias is deeply hidden, and our training data comes directly or indirectly from some form of human subjectivity. The more autonomy plays a role in our lives, the more aware we need to be of how internal bias from human source has affected things like predictions. And please, there is nothing more flippant and off-putting than railing against "SJWs," this is a topic that demands thoughtfulness.
- unstuckdev 8y ago
  I still see people call policing maps "crime maps" as though you could map out all the crime like in SimCity.
- tomp 8y ago
  > The problem is that people view pure statistical based crime probability as "unbiased" because... math. It's not! Can you explain what you mean? “Biased” usually means not using just statistics but also extra-statistical personal convictions. What does “unbiased” mean for you?
  - nkozyra 8y ago
    Where do the numbers come from? What is the context of collection of data? What is the context of the classification of training data? There are hundreds of questions like that which apply. It's not (necessarily, it certainly can be) the algorithm that carries bias, it's the data.
- pokemongoaway 8y ago
  Your reply was not thoughtful at all. All you're doing is pushing a belief without evidence. Where exactly is this bias hidden? Saying that you don't have it and others do should require some citation as well - and is incredibly self-righteous. I don't see anything wrong with criticizing SJWs in a political post by SJWs!
majos 8y ago
> Unequal outcome != bias. Sure, which is why the fair machine learning literature has developed more nuanced (and IMO better) definitions of fair predictors. For example, suppose you have some sensitive attribute A (say race) and want to know some outcome Y. A predictor \hat Y satisfies equalized odds if \hat Y and A are conditionally independent given Y. In binary classification, this means that the classifier has identical false positive rates and false negative rates across races. So e.g. your false negative rate for white people must be the same as your false negative rate for black people. Note that a perfectly accurate classifier satisfies equalized odds -- roughly, its a way of guaranteeing that errors are not concentrated in some group. I think it's unfair to characterize fair ML research as the work of "SJWs". There's a lot of smart, technical work done by smart, technical people thinking hard about these problems. Moritz Hardt's course on the topic is still a decent intro: https://fairmlclass.github.io/ https://fairmlclass.github.io/
- tomp 8y ago
  Oh that’s interesting! So you can have your “creditworthiness AI” \hat Y) predict that disproportionately more black (A) people are not creditworthy, as long as that’s (roughly) in line with the actual creditworthiness (Y) as determined by the “supervisor”.
  - majos 8y ago
    That's also a good point -- evaluating fairness against labelled data breaks down if the labels themselves are biased in some way. IMO (and I am not familiar with the entire fair ML literature), we still lack technical tools to address biased data well.
    - tomp 8y ago
      I didn’t mean that they’re biased, just that being “black” correlates with “being poor”, which is not biased in any way but might still appear so based on superficial view(well, the data/AI isn’t biased, though the society might be but that’s not the bank’s problem...) Edit: biased not busy.

nkozyra 8y ago

The problem is that people view pure statistical based crime probability as "unbiased" because... math. It's not! Most bias is deeply hidden, and our training data comes directly or indirectly from some form of human subjectivity. The more autonomy plays a role in our lives, the more aware we need to be of how internal bias from human source has affected things like predictions. And please, there is nothing more flippant and off-putting than railing against "SJWs," this is a topic that demands thoughtfulness.

unstuckdev 8y ago
I still see people call policing maps "crime maps" as though you could map out all the crime like in SimCity.
tomp 8y ago
> The problem is that people view pure statistical based crime probability as "unbiased" because... math. It's not! Can you explain what you mean? “Biased” usually means not using just statistics but also extra-statistical personal convictions. What does “unbiased” mean for you?
- nkozyra 8y ago
  Where do the numbers come from? What is the context of collection of data? What is the context of the classification of training data? There are hundreds of questions like that which apply. It's not (necessarily, it certainly can be) the algorithm that carries bias, it's the data.
pokemongoaway 8y ago
Your reply was not thoughtful at all. All you're doing is pushing a belief without evidence. Where exactly is this bias hidden? Saying that you don't have it and others do should require some citation as well - and is incredibly self-righteous. I don't see anything wrong with criticizing SJWs in a political post by SJWs!

majos 8y ago

> Unequal outcome != bias. Sure, which is why the fair machine learning literature has developed more nuanced (and IMO better) definitions of fair predictors. For example, suppose you have some sensitive attribute A (say race) and want to know some outcome Y. A predictor \hat Y satisfies equalized odds if \hat Y and A are conditionally independent given Y. In binary classification, this means that the classifier has identical false positive rates and false negative rates across races. So e.g. your false negative rate for white people must be the same as your false negative rate for black people. Note that a perfectly accurate classifier satisfies equalized odds -- roughly, its a way of guaranteeing that errors are not concentrated in some group. I think it's unfair to characterize fair ML research as the work of "SJWs". There's a lot of smart, technical work done by smart, technical people thinking hard about these problems. Moritz Hardt's course on the topic is still a decent intro: https://fairmlclass.github.io/ https://fairmlclass.github.io/

tomp 8y ago
Oh that’s interesting! So you can have your “creditworthiness AI” \hat Y) predict that disproportionately more black (A) people are not creditworthy, as long as that’s (roughly) in line with the actual creditworthiness (Y) as determined by the “supervisor”.
- majos 8y ago
  That's also a good point -- evaluating fairness against labelled data breaks down if the labels themselves are biased in some way. IMO (and I am not familiar with the entire fair ML literature), we still lack technical tools to address biased data well.
  - tomp 8y ago
    I didn’t mean that they’re biased, just that being “black” correlates with “being poor”, which is not biased in any way but might still appear so based on superficial view(well, the data/AI isn’t biased, though the society might be but that’s not the bank’s problem...) Edit: biased not busy.