The researcher's job is to make progress towards the research goal. Progress that doesn't solve the problem is still progress. Nobody's saying that the PULSE authors have solved their research goal of generating white faces. It's why papers have a "discussion and future work" section, which touched on the issue in the first version of their paper. It's why in their revised paper, they added a full discussion of bias and the issues in the current model. They did their job, but didn't solve the whole problem, which is too high a bar for any researcher. Science is incremental.
That's how I interpreted LeCun's distinction between the researcher's job and the product builder's job.
As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.
For someone like Yann specifically? Publicly state that the ML scene is optimizing in a myopic way, and invest in doing so less myopically. For example, I think the translation space has a clear goal and the right goal and is making strides in improving language models in many useful ways.
Ultimately, if there aren't ethical and useful ways to apply facial recognition, leaders should be steering people away from those research topics.
No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. So if somebody builds an AI system that fails on out-of-sample data, then says that had they tried to do it properly they would have succeeded, that's a pretty lame excuse for a poor AI system. They didn't even do their job in the narrow sense that you're using, you don't even need to consider the "social biases" or whatever, it's just a system that didn't work. In fact, many years into this research program, "focussing on his task and correcting it" (working on all human faces) is still not done given the current performance of these systems, but they are quite sure it can be done if they tried.