I was joking, but probably so, to the extent that 80% of peer reviewers are men and 80% of authors of peer reviewed articles are men[0]
0. https://www.jhsgo.org/article/S2589-5141%2820%2930046-3/full...
That's a dream which is unlikely to come true.
One reason being that the training data is not unbiased and it's very hard to make it less biased, let alone unbiased.
The other issue being that the AI companies behind the models are not interested in this. You can see the Grok saga playing out in plain sight, but the competitors are not much better. They patch a few things over, but don't solve it at the root. And they don't have incentives to do that.
The best you can hope for is to provide technical means to point out indicators of bias. But anything beyond that could, at worst, do more harm than good. ("The tool said this result is unbiased now! Keep your skepticism to yourself and let me publish!")
Bias is systematic error.
Maybe your thermometer just always reads 5° high.
Maybe it reads high on sunny days and low on rainy days.
Bias is distinct from random error, say if it's an electronic thermometer with a loose wire.
For classification problems, there's also this impossibility result: https://www.marcellodibello.com/algorithmicfairness/handout/...
Source: I've personally been involved in peer reviewing in fields as diverse as computer science, quantum physics and applied animal biology. I've recently left those fields in part because of how terrible some of the real-world practices are.