4 ms·
I think it's not inherently bad to have a bias, but it's bad if you don't recognize it. In your example: If you train an AI on data that solely consists of tra
by bcyn 9y ago
I think it's not inherently bad to have a bias, but it's bad if you don't recognize it. In your example:
If you train an AI on data that solely consists of trading firms & banks, you should recognize that it's an AI biased towards detecting activity at trading firms & banks, and that it might be lacking in other areas.
It becomes dangerous when such an AI is marketed and assumed as unbiased and the source of truth for detecting all insider trading activity.
- mc32 9y agoIf we don't have infinite resources (prosecutorial, for example), it makes sense to concentrate on the most salient loci of crime. If after enough resources are devoted to the most egregious locus, a different locus becomes the focus of crime and in turn gets the attention, that's not a bad thing... In other words if done decently well, they'd obtain data from all places but focus on the problematic areas till they reach equilibrium and then you redirect to the next hotspot, no?
- evgen 9y agoIs it a loci because that is where the crime happens, or is it a loci because that is where all of the crime prevention/detection/prosecution is directed due to external circumstances? You have to be able to correct for this sort of bias in your training data or else you are just baking the external circumstances into the model and pretending that they reflect reality. Start by figuring out how you would obtain data from all places first.