Has your machine really learned something? Snap quiz time Machine learning (ML) is all about getting machines to learn but how do we know how well they are doing? Answer – confusion matrices and ROC space. Suppose we have existing data about people who buy books from our website – age, amount spent, preferred author and so on. These columns of data are known, in ML terms, as the " …
"Not everyone is male or female"
that's not actually true.
in general you have ether 2 x chromosomes or an x and a y chromosomes which will make you female or male respectively. There are some genetic disorders where you can have additional chromosomes or or even have a female genotype and have phenotypically male characteristic, or someone with a male genotype has AIS so will impair the masculinization of male genitalia and may appear more feminine,,, it still does not change the fact they are ether genetically male or female. There are other genetic abnormalities where some people can have xxyy or xyy, both genetically are male, and there is xxx and even xxxx who still remain genetically female.
now if you want to talk about what gender people self identify as, that's a whole different story.. Its entirely up to them what they self identify as and living in the enlightened times we do, we accept that people may decide what gender they want to identify as and society will (quite rightly) let them get on with it.
If the customer is transgender, your algorithm will probably identify them as the gender they actually are rather than the one on their birth certificate, but they might, for various reasons, have told you that it is the one on their birth certificate. Then you will have marked the computer as wrong, when actually it wasn't.
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