It's not racist show, point out, or claim that data racially skews in one direction. If that were true, then you couldn't even claim that minorities are under privileged. Right? Then how could you help them if you aren't able to recognize the areas that the biggest challenges? You're right when interpreting this way.
But the thing you do care when you want to attribute causality. In part this is an issue because people naturally associate correlation with causation (there is good reason but that's a long discussion. See Judea Pearl's The Book of Why). At the end of the day, we really are always after causal relationships, because we want to do things with the data (somewhere along the chain). So it's not that you want to remove race from data, but rather that you want to be wary and ensure that your variable is not confounding the real issue. Though this happens outside of race too.
And note that at times there where race does play a causal role. (I suspect not likely in the parent's case) For example, different races may be more prone to certain illnesses or genetic disorders.
If it helps, maybe it is easier to frame it as it's easy to be lazy, but the pressure around race makes us more likely to revisit our analysis and look for confounding variables. The thing is, this will improve your stats even for the non-minority settings because the truth of what you're (hopefully) doing, is just making better models.
I think we're getting at roughly the same idea here, I don't actually think its about racism at all though. The parent commenter clearly didn't select a population based in their race or sex. This may be an unpopular opinion, but I have always viewed racism as very much linked to the person's intent, something that may unintentionally have a bias isn't racist in my book and really lumping it in would cheapen the argument when the intent really was there.
That said, in this case I really am curious if it turned out that there was an unintentional bias somewhere. Say they ended up with 90% black women and in reality that is only say 40% of the total population of kids with regular interactions with the judicial system. There's certainly something there they missed and it'd be interesting data to understand how that happened even when they purposely ignored race and sex. It usually boils down to an otherwise benign detail of the selection process that makes way more of an impact than would have been expected.
> I have always viewed racism as very much linked to the person's intent
Certainly this is one of the most critical factors. I'd argue that this is critical in being not racist in the first place, since it is easy to misinterpret actions. Not to say that how it is received doesn't matter, but that we're in a globally interacting community and we're bound to step on one another's feet, so it is important to recognize that not knowing the dance is not the same as intentionally attacking nor that not knowing the dance is inexcusable in the first place. As this would need to go both ways and then we'd all be at fault. We should try to learn at least.
But I do want to push back a bit, and I think this connects to the prior point of the statistics. I do think it is possible to be unintentionally racist. Just like you can intentionally harm someone and you can unintentionally do so. In either case harm is still done, right? But it is with good reason we distinguish these in our legal system.
The world is much more complicated than it used to be, and this is the burden of advancement. As we advance, lower order approximations are no longer sufficient to solve problems, so we must become more nuanced, more forward thinking, and we must slow down so that we can move fast. The burden of our advancements is that we are now the gods who destroy cities without even knowing what we have done. The same way a butterfly does not know the hurricane it creates, because it's the interaction of its actions combined with so many others. But this does not change the end result. A more connected world means that our actions have more paths to travel through, and thus can do more. The question more is if we'll deny this or if we will try to do better. Maybe it is impossible to live without stepping on cities, but even if that is true, it doesn't mean we shouldn't try to look where we step and minimize the damage.
So what I'm saying is to me it didn't sound like they were trying to deny the correlation of whatever they were studying had to do with race, but rather that we've advanced as a society enough that correlation is insufficient. And at least in this domain we've recognized how it is easy to fool ourselves with data. Because I'll tell you, most people fool themselves with data, including experts. The difference is the expert always reserves some doubt. Most people confuse what data analysis does. It doesn't answer questions, it can't. Instead it eliminates potential answers. If you remember this, doubt is a natural consequence. If you don't, you'll always be the fool, lying to yourself.
But the thing you do care when you want to attribute causality. In part this is an issue because people naturally associate correlation with causation (there is good reason but that's a long discussion. See Judea Pearl's The Book of Why). At the end of the day, we really are always after causal relationships, because we want to do things with the data (somewhere along the chain). So it's not that you want to remove race from data, but rather that you want to be wary and ensure that your variable is not confounding the real issue. Though this happens outside of race too.
And note that at times there where race does play a causal role. (I suspect not likely in the parent's case) For example, different races may be more prone to certain illnesses or genetic disorders.
If it helps, maybe it is easier to frame it as it's easy to be lazy, but the pressure around race makes us more likely to revisit our analysis and look for confounding variables. The thing is, this will improve your stats even for the non-minority settings because the truth of what you're (hopefully) doing, is just making better models.