People are studying ways to train accurate labels with noisy labels (see the paragraph in this article: https://tmabraham.github.io/blog/noisy_imagenette#Prior-rese...)

Also, technically accuracy as a metric is robust to noise (https://arxiv.org/abs/2012.04193). That means that a model with the highest accuracy on a noisy dataset will likely be the best model on the clean dataset. So these noisy datasets can still be very useful for the development of deep learning models. In fact if you look at the tradeoffs between getting larger datasets that have noisy labels vs. smaller datasets will clean labels (since good annotation is expensive!), the noisy large-scale dataset will probably be more useful.