This is an extremely unpleasant position to take, if your point of view is empowered within the status quo. It is much extra work for no discernible benefit to the researcher.
If your point of view is subject to disproportionate suffering under the status quo, then reinforcing current practices by implicitly enshrining them in input datasets will make improving your situation even harder.
As an example, consider the case of public school funding. In a hypothetical system where school resources are provided proportionally based on student success, good schools will thrive and bad schools will get worse. If someone points out this isn't fixing the problem, you can reverse the proportions -- this will cause good schools to suffer while bad schools will get additional funding (disincentivizing student success). In cases like this, it's not enough to just have a purely abstract set of metrics on which to base resource allocation: it will always require actual investigation of why good schools produce good results and why students do poorly in specific schools.
This is sort of obvious, of course, but it isn't being translated into terms that some researchers can or will grasp. It's never enough to just tell someone to 'debias the dataset,' as determining that bias is a monumentally difficult challenge that people have failed to achieve for many generations. A key factor in fact is the propensity for this kind of research to get deployed, today, by people who are not experts in a given domain of investigation, with possibly disastrous results in policymaking. These tools are not abstractions that require a team of experts to translate from research paper to the real world; ML researchers put out results that you can shove into your nearest computer and run.
What Timnit and others are getting at is that it requires thoughtful and careful assessment to get real value out of this sort of research. Ideally, in Timnit's assessment, the researchers themselves would put effort into identifying possible calamities and put as much effort into mitigating them as they do into publicizing the work itself.
Yann LeCun and other researchers simply do not believe this is their responsibility; all they want to focus on is the mathematics themselves. I'm sympathetic to this position but I also very much do understand the opposition. One of my favorite movies from childhood, "Real Genius," deals with this sort of issue as the main plot line.