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It's not quite like that. Though blaming algorithms for being "biased" is rather strange as well. It seems to me the algorithms do exactly what you'd expect th

by Tharre 10y ago

It's not quite like that. Though blaming algorithms for being "biased" is rather strange as well. It seems to me the algorithms do exactly what you'd expect them to: have predictive capabilities about reality. Of course, there is a problem here. You don't train neural networks with data of people who've committed crimes. You don't have such data. So what data has been used by the engineers to feed to this machine? Data of people who have been convicted of committing a crime, mostly likely. And now you've constructed a feedback loop. Those pesky blacks are more likely to commit crimes? Always knew it. A man has been murdered and there are 2 suspects, one white and one black? Why even bother investigating the white guy, we already knew it was the black one. Holy cow, 1 year with the new system and black crime has increased by 50%! Always knew it. Better keep the system in place to save everyone from those pesky blacks. Oh, the irony.

jjoonathan 10y ago
I understand your/TFA's argument but I think falls dangerously wide of the mark. I'm sure that the feedback process you describe is real, but I'm also sure that judicial prejudice is far from the only thing feeding the cycle of poverty and crime that plagues black communities. Even if we were to succeed at eliminating judicial prejudice from our training data, the other factors maintaining the cycle would once again lead the ML algorithm right back to p(criminal|black)>p(criminal). The way to fight the prejudice is not to pretend that math would work out differently if only we had better data / more sophisticated models / etc, it's is to realize that it's OK to discard the result of bayesian reasoning due to factors out of the calculation's scope. Courts discard evidence all the time for a million sundry reasons. Racism / double standards can be another.
- cerebrum 10y ago
  What other data do we discard? Who gets to decide which data to discard? There is no point in doing data analysis if you then discard uncomfortable data anyways.
  - jjoonathan 10y ago
    Discarding evidence isn't a new concept, it's a well established and very import part of our existing judicial system. Lady Justice doesn't wear a blindfold as a fashion accessory. Google "fruit of the poisonous tree" to learn about the specifics.

by Tharre 10y ago

jjoonathan 10y ago

I understand your/TFA's argument but I think falls dangerously wide of the mark. I'm sure that the feedback process you describe is real, but I'm also sure that judicial prejudice is far from the only thing feeding the cycle of poverty and crime that plagues black communities. Even if we were to succeed at eliminating judicial prejudice from our training data, the other factors maintaining the cycle would once again lead the ML algorithm right back to p(criminal|black)>p(criminal). The way to fight the prejudice is not to pretend that math would work out differently if only we had better data / more sophisticated models / etc, it's is to realize that it's OK to discard the result of bayesian reasoning due to factors out of the calculation's scope. Courts discard evidence all the time for a million sundry reasons. Racism / double standards can be another.

cerebrum 10y ago
What other data do we discard? Who gets to decide which data to discard? There is no point in doing data analysis if you then discard uncomfortable data anyways.
- jjoonathan 10y ago
  Discarding evidence isn't a new concept, it's a well established and very import part of our existing judicial system. Lady Justice doesn't wear a blindfold as a fashion accessory. Google "fruit of the poisonous tree" to learn about the specifics.