Isn't that asking too much. How would a researcher also know how mitigate the problems they identified.
The whole paper was a nothing burger wrapped in social justice language with asides about how global warming is Actually Racism because of disparate impact (interesting, not an ML topic).
If the problems aren't novel and you're proposing zero solutions, it shouldn't be a paper.
I wonder when the “performative wokeness” bubble will burst.
You're right, this wouldn't have happened if she were a straight white male - if she were a straight white male, there wouldn't be comments like yours making asinine assumptions about how good she was at her job.
If anything, straight white males are trashed harder around here.
That's the only problem I've ever had with having a woman on the team: the fear of saying something that offends someone, her or other.
Poorly written papers are a regular occurrence in any field - that doesn't justify attributing bad faith or character issues to Dr. Gebru.
These are ridiculous claims, and it’s fair to respond to them by saying, “well, what exactly do you imagine a solution or mitigation looks like?”
Essentially, by the nature of how specious Gebru’s stated problems are, they demand clarity over what an “ethical solution” even is, conceptually, and why everyone would have to agree.
For example, you could discuss economies of scale or train-once-finetune-everywhere approaches with GPT that reduce total energy needs. Or you could discuss how researchers can register the corpus they use and the snapshot of time it was grabbed, with an open understanding that as long as the methods and data are reproducible, there is no research ethical issue with studying that corpus, no matter how much bias or lack of woke vocab a given person believes it has. (And also, nobody is required to just accept activist language as important or valid.)
Gebru did none of this. The article could literally be summed up by Gebru saying, “I think <supposedly shocking evidence> is bad, therefore its connection to something in ML is bad.”
E.g. “I think, subjectively, that the raw energy use to train GPT is bad. Here are some shocking comparisons. Therefore GPT is bad.”
It’s incredibly unrigorous and juvenile. Dean’s comments that it needs to clearly state mitigations is actually a super generous, polite way of saying the paper is just subjective amateur hour.