These issues could be caught by carefully inspecting the intermediate data between every normalization step for every new model trained.
we avoid this approach for three reasons: privacy considerations; the sheer number of language models across languages (each one with many data sets that undergo many steps of normalization); and wanting staff to be able to develop improvements to models even when they do not speak the language involved.