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> provided they learned on a representative sample From this, I think you're talking about deep learning algorithms. However, the term algorithm predates prac

> provided they learned on a representative sample From this, I think you're talking about deep learning algorithms. However, the term algorithm predates practical AI, deep learning, neural nets, etc. I would know because I sat for an algorithms exam when the fastest consumer computers had speeds measured in single-digit MHz. An algorithm is essentially just a series of steps that a person (or more commonly now adays, a computer) can follow along with to achieve a specific goal. Talking about stochastic systems, back propagation, or even simple probability and confidence intervals just muddies the issue. At its core, algorithms can merely be something defined deterministically, and programmed by hand by a human being. By the way, the system I described isn't something in actual use by machines (I hope). It's just the thought pattern of biased people in my country, a sort of mental short cut used to determine what level of service you get.

throwaway894345 6y ago
I thought we were talking about machine algorithms, but it doesn't really matter. We're talking about a simple statistical mapping between two boolean properties: skin tone and citizenship. You stipulated already that, for a given light-skinned person, the algorithm (machine or human) will return "non-citizen" with 99% confidence, and that that confidence interval is correct. As far as I can tell, we can conclude from those facts that there is no bias (maybe I'm wrong, but I consider bias to be an errant belief); however, there are still potential issues: * For what applications is 99% confidence acceptable? Can we do better by avoiding a statistical analysis altogether, e.g., by using citizenship records? * Do we really believe/trust that humans are capable of 99% confidence (calling into question the fact that you established in your original post)? * Are users of this algorithm rounding 99% up to 100%, thus disenfranchising one group 1% of the time (as opposed to distributing the error proportionately between the two groups)?

throwaway894345 6y ago

I thought we were talking about machine algorithms, but it doesn't really matter. We're talking about a simple statistical mapping between two boolean properties: skin tone and citizenship. You stipulated already that, for a given light-skinned person, the algorithm (machine or human) will return "non-citizen" with 99% confidence, and that that confidence interval is correct. As far as I can tell, we can conclude from those facts that there is no bias (maybe I'm wrong, but I consider bias to be an errant belief); however, there are still potential issues: * For what applications is 99% confidence acceptable? Can we do better by avoiding a statistical analysis altogether, e.g., by using citizenship records? * Do we really believe/trust that humans are capable of 99% confidence (calling into question the fact that you established in your original post)? * Are users of this algorithm rounding 99% up to 100%, thus disenfranchising one group 1% of the time (as opposed to distributing the error proportionately between the two groups)?