[jackpirate]

Your response doesn't at all address gbrown's example of how a misapplied algorithm can exacerbate a systemic inequality. Specifically, you provide an example of gbrown's points 1 and 2 while ignoring point 3, which is the crux of the issue.

[Excel_Wizard]

The detail "This is assuming a perfectly-trained AI judges the variable, and that it controls against the underlying variables of societal bias in order to ensure that long-term human resources are properly allocated, and that long-term biases, informed by people's interactions with eachother, trend toward actual biological differences" should address that concern.

[gbrown]

"Assume a perfect and impossible thing, which is not precisely defined enough to formally reason about, but which nevertheless by construction supports my point, and you'll see that I'm clearly correct."

"controls against...." doesn't mean anything here - you're using the language of modeling, and attempting to discuss a concrete issue, but not connecting the two. Is your goal just to get an accurate prediction based on the status quo? Congrats, you've got a model that still requires nuance and understanding in application.

[Excel_Wizard]

Well, to create a well-enough formalized method of control for the AI-

As part of a 1,000,000 year long project, 1,000,000 groups of 1,000 human babies (with different groups having different gender ratios) are installed on remote habitable planets and raised from birth by genderless robots, tabula rasa. They grow up and form languages and societies that last for 1,000,000 years. Robots are used to observe their choices and outcomes. The distribution of cultural traits is gathered as data, and cultures which create good outcomes for individuals in accordance with their preferences and for society as a whole are noted as benefit-maximizing. Additionally, the degree to which each society deviates from mean gender-bias characteristics is noted, and the degree to which these gender-bias expectations mold the choices of each individual to a degree greater than mean gender-bias is noted. This data is used to train the "sorting hat" robot which will be used in the example in my original post.