This is fundamentally a forest/trees type of situation. LeCun sees the issue with this one model, says, "If this had been trained on a different sample it wouldn't have this issue," and stops his train of reasoning there. The problem he is seeing is the mis-trained model, and nothing more.
But the problem is larger than this single model, because this issue (or similar ones) are pervasive in the fields in which AI are being employed. If a neural net is helping a court hand down sentences, it is going to be trained on historical sentencing data, and will in turn reflect the biases present in that data. If you are still only seeing the one tree, you say "well we must correct for the historical bias," and absolve yourself of thinking of the larger problem. That forest problem is that we will always be feeding these algorithms biased inputs, unless we do the work to understand social biases and attempt to rectify them.
But the problem is larger than this single model, because this issue (or similar ones) are pervasive in the fields in which AI are being employed. If a neural net is helping a court hand down sentences, it is going to be trained on historical sentencing data, and will in turn reflect the biases present in that data. If you are still only seeing the one tree, you say "well we must correct for the historical bias," and absolve yourself of thinking of the larger problem. That forest problem is that we will always be feeding these algorithms biased inputs, unless we do the work to understand social biases and attempt to rectify them.