I want to start here because I think it matters more than the workflow itself.
Hiring is not like donor segmentation.
When AI gets hiring wrong, you can end up excluding a genuinely great candidate, or worse, systematically excluding a whole category of candidates without ever realizing that it happened.
This is a well-documented problem outside the nonprofit sector already.
AI tools trained on historical hiring data can learn and repeat the biases baked into who got hired in the past.
If your sector or your organization specifically has a history of hiring a narrow type of person for development roles, a tool that's trained naively on that pattern will tend to reproduce it invisibly without ever flagging that it's doing so.
So before I even get into what I actually did, I want to name the principle that shaped the whole process.
It never touched the parts that were about judgment on a human being.
I want to start here because I think it matters more than the workflow itself.
Hiring is not like donor segmentation.
When AI gets hiring wrong, you can end up excluding a genuinely great candidate, or worse, systematically excluding a whole category of candidates without ever realizing that it happened.
This is a well-documented problem outside the nonprofit sector already.
AI tools trained on historical hiring data can learn and repeat the biases baked into who got hired in the past.
If your sector or your organization specifically has a history of hiring a narrow type of person for development roles, a tool that's trained naively on that pattern will tend to reproduce it invisibly without ever flagging that it's doing so.
So before I even get into what I actually did, I want to name the principle that shaped the whole process.
It never touched the parts that were about judgment on a human being.
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