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Notes -
Isn't part of the problem that 'diversity' is somewhat fundamentally at-odds with 'next likeliest token' (or the equivalent for image generation models)? Except for whatever thermal noise is being added intentionally (which should be small) and active efforts to the contrary (which is, I think, dominating what we're seeing), the model isn't wrong to assume that "draw a person" merits a response that looks like a modal person.
Expecting "[minority fraction] of the outputs should look like [minority]" is maybe not completely crazy, but doesn't seem to align with the math as far as I'm aware. Nor is it even necessarily well-defined: which population? Should "draw an NBA player" match the NBA's demographics? Should it draw all players equally likely, or weight towards popular ones? Do we just mean current players? These are questions that have mostly been sidestepped for representation in political arenas --- affirmative action never has been asked to specify specific percentage targets, nor do I think it could do so without controversy. But for large scale computer-automated systems, it's not hard to start running cross tabs for things and finding imbalance everywhere. Not even sure myself what to do about all of that.
Its a tricky subject and the best response should be "shrug shoulders and blame Long Dead History" because when you tip the scales with your own grubby fingers its your fingerprints all over the black female wehrmarcht soldier or male asian confederate senator.
OR
go the way of the Greendale Human. Representation via eldritch horror
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