But that seems hard to implement - you'd have to recalculate this for every candidate every time you got a new candidate - so perhaps we can implement an optimization: approximate this by estimating the distribution of the group and measuring differences from some centroid.
HR teams, I'm available for techwashing consultancy.
Nice! Especially if coupled with a secondary business whereby you train applicants on how to increase their "diversity points"
EDIT: I think I have the appropriate "one weird trick": flood the pool with fake applications grouped together but very dissimilar to you.
Even if the writer did, it still isn't reasonable for a million readers to presume that a writer meant anything other than what they wrote, according to the consensus usage of the the words/phrases in the given context.
It only makes sense to assume the writer meant what most readers would interpret they meant, and don't move off of that assumption unless the writer issues some update or correction.
So they artificially skew the participants by granting bonus points for any minority groups.
[1]in soul if not title