[noetic_techy]

I'm not a ML guy, but reading this, it almost sounds like the training data needs to be a fictional, idealized set, and not based on real world data that already has bias slants built in. Possibly composites of real world candidates with idealized characteristics and fictional career trajectories. Basically, what-my-company-looks-like vs what-I-want-it-to-look-like. I'm not sure this is even possible.

Its an interesting questions. On one hand, a practical person could argue: "Well, this is what my company looks like, and these are the types of people who fit with our culture and make it, so be it. Find me these types of candidates."

VS

"I don't like the way may company culture looks, I would rather it was more diverse. This mono-culture is potentially leaving money on the table from not being diverse enough. I'm going to take my current employees, chart their career path, composite them (maybe), tweak some of the ugly race and gender stats for those who were promoted, and feed this to my hiring algorithm."

[ergothus]

> the training data needs to be a fictional, idealized set, and not based on real world data that already has bias slants built in

Thatd be great, but in this case (as in most ML cases) the idea is not "follow this known, tedious process" but instead "we have inputs and results but dont know the rules that connect them, can you figure out the rules?"

> this is what my company looks like

In tech hiring, no one wants the team they have...they want more people but without regrets (including regretting the cost)