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Big data may conclude that the data shows black people are more likely to commit crimes, but it doesn’t tell you the why, which may be that black people are sys
by chrischen 8y ago
Big data may conclude that the data shows black people are more likely to commit crimes, but it doesn’t tell you the why, which may be that black people are systematically opressed, more likely to come from a poorer background, discriminated upon so as to reduce their options in life. The AI, based on statistics can tell you the effects, but not the causes.
So if i were to go and beat up every person named John, and then machine learning takes a crack at it and tells us that people named John are more likely to get injured, we may end up discriminating against Johns without realizing it was I who just have a thing against people named John. If this happens in a system and creates a feedback loop, It can lead to something becoming a self-fulfilling prophecy when it need not have been.
Based on a naive conclusion the solution may be to deny health coverage to people named John, but obviously the real solution is to put me in jail.
- ekianjo 8y agoRegardless of the data used, not knowing the why does not invalidate a strong correlation at all. If you stopped at doing thing only when you know the why there would no progress in science, because there are always new WHYs you uncover as you go.
- tanana 8y agoThat's why when studies that make discoveries of new correlations, they have to replicate such findingers in different study designs that actually allow you to say something about causation. In medicine, that would often mean a randomized study between treatment A and a placebo. However, with factors such as race, or sex this is obviously impossible.
- ekianjo 8y agoIntead of looking at race or sex, looking at individual behaviors usually explains things better but it is harder to capture.
- curiousgal 8y ago> which may be that black people are systematically opressed, more likely to come from a poorer background, discriminated upon so as to reduce their options in life Also know as a cofounding effect. All of these problems have been encountered by statisticians decades ago.
- tanana 8y agoCorrecting for confounding is probably the hardest thing to do when you are building statistical models. There is no true consensus with regard to how you should go about correcting for confounding. Did you know that if you correct wrongly for a confounder, you actually introduce bias? As a very modest medical researcher myself, I have become very careful about the conclusions I make with any model I make. While it is very appealing to make conclusions about causation, they are very often wrong. For more information, I think this might interest you: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...
- dqpb 8y agoThe Book of Why https://www.amazon.com/Book-Why-Science-Cause-Effect https://www.amazon.com/Book-Why-Science-Cause-Effect