In that case, let’s look at the differences. In the case of ML researchers, careers are built on publications. I imagine a hacker who discovers a big vulnerabil

In that case, let’s look at the differences. In the case of ML researchers, careers are built on publications. I imagine a hacker who discovers a big vulnerability in their employer probably would be burning bridges if they published the vulnerability before the employer could fix it. In AI ethics, or at least the most notorious recent case, it seemed like publishing was the primary function and change was secondary. Then there’s the ease of changing things. In security, I’d imagine fixing a vulnerability might require lots of work, but rarely novel research. In ML that’s not the case. and after all that research, the finding might be a way to improve the worst cases by worsening the average cases, lowering revenue. And then there’s the clarity of what even is an issue. If someone shouldn’t be able to access a file, but they can, it’s clear. The goal then is convincing stakeholders that the problem should be prioritized. In AI ethics, you have to convince them that the problem even is a problem, convince them to allocate research, then convince them that the technique is worth lowering revenue for, and only then get to the question of allocating resources to implement the fix. Convincing stakeholders to prioritize fixes is a wholly different game. I like your analogy, and there are surely lessons to be learned (e.g. maybe the ethics researchers’ primary job shouldn’t be publishing papers), but the differences are strong enough that the lessons might not extend too far.