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this post was submitted on 06 Jul 2023
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I figure you’d audit it by examining the results, and if bias isn’t detectable in the results then I’d argue that’s at the very least still better than the human-based systems we’ve been relying on up til now.
Unequal outcomes isn’t evidence of bias.
Not inherently, but things can be tested.
If you have a bunch of otherwise identical résumés, with the only difference being the racial connotation of the name, and the AI gives significantly different results, there's an identifiable problem.
That makes sense: empirical tests of the AI as you describe.
What would unbiased results look like?
When the demographics of the output are roughly equivalent to the demographics of the input. If ten men and fifty women apply, and eight men and two women are hired, that is worth investigating.
That would be a pretty extreme bias to have, so yeah that would make sense. If it’s not so drastic it might be harder to spot by just looking at the results.
It’s a flag, not the entire investigation. If something seems suspicious, that’s the queue to investigate, not just immediately slap cuffs on someone.
Right, that’s why I was trying to ask you for your opinion on what the threshold for “investigation worthy” results
I’m not a policy expert, author of the bill, or in charge of the department that will lead these investigations. Even if I were an expert on the subject, what I’d do and what this department will do aren’t likely to be the same.
I just support civilian oversight and audits of these algorithms and LLMs as they take up a more prominent position in hiring and firing.
I was just asking for more info on how you’d examine the results for bias since it would need to be pretty extreme like the example you gave to be identifiable as something worth investigating.
All good if you don’t have a more specific answer, I was just curious what your personal thoughts were here.