Comment by no_wizard
2 days ago
It isn’t. This is dismissive without first thinking through the difference of application.
AI safety is about proactive safety. Such an example: if an AI model could be used to screen hiring applications, making sure it doesn’t have any weighted racial biases.
The difference here is that it’s not reactive. Reading a book with a racial bias would be the inverse; where you would be reacting to that information.
That’s the basis of proper AI safety in a nutshell
As someone who has reviewed people’s résumés that they submitted with job applications in the past, I find it difficult to imagine this. The résumés that I saw had no racial information. I suppose the names might have some correlation to such information, but anyone feeding these things into a LLM for evaluation would likely censor the name to avoid bias. I do not see an opportunity for proactive safety in the LLM design here. It is not even clear that they even are evaluating whether there is bias in such a scenario when someone did not properly sanitize inputs.
> I find it difficult to imagine this
Luckily, this is something that can be studied and has been. Sticking a stereotypically Black name on a resume on average substantially decreases the likelihood that the applicant will get past a resume screen, compared to the same resume with a generic or stereotypically White name:
https://www.npr.org/2024/04/11/1243713272/resume-bias-study-...
That is a terrible study. The stereotypically black names are not just stereotypically black, they are stereotypical for the underclass of trashy people. You would also see much higher rejection rates if you slapped stereotypical white underclass names like "Bubba" or "Cleetus" on resumes. As is almost always the case, this claim of racism in America is really classism and has little to do with race.
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> but anyone feeding these things into a LLM for evaluation would likely censor the name to avoid bias
That should really be done for humans reviewing the resumes as well, but in practice that isn't done as much as it should be
If you're deploying LLM-based decision making that affects lives, you should be the one held responsible for the results. If you don't want to do due diligence on automation, you can screen manually instead.