3 ms·

For example, the model under discussion, which can tell the difference between men and women and believes in differences between them, probably doesn't know tha

For example, the model under discussion, which can tell the difference between men and women and believes in differences between them, probably doesn't know that intersex people exist. It probably cannot learn such a distinction, either. Therefore this algorithm probably doesn't fit what we know reality to resemble. So there's at least two sorts of biases. There's both biases inside the model, which is what you're thinking of when you use the word "bias", and also biases outside the model, which inform the model's design and construction in ways that cannot even be quantified with only the model's metrics.

rumanator 7y ago
> For example, the model under discussion, which can tell the difference between men and women and believes in differences between them, probably doesn't know that intersex people exist. Considering that the frequency of intersex people in a population lies somewhere between 0.05% and 0.07%, not accounting for a secondary trait such as whether a biological M or F happens to be intersex is an irrelevant classification error that has a negligible (if any) impact on a model's predictive ability. And by the way, any model can be regenerated if any attribute left out is found to be significant.