I think your point was a straw man. You can tell if your model is good by measuring its predictive power on data outside your training set. You seemed to be a

I think your point was a straw man. You can tell if your model is good by measuring its predictive power on data outside your training set. You seemed to be arguing that only way to know the model is wrong is to insert a human who just knows a priori that the model is wrong and what "correction" to apply. But I think this is a false analysis. You can measure a model's accuracy by seeing how well it predicts data outside the training set. If the model is highly predictive, you have a good model. It doesn't take a human's subjective analysis or principled intervention to determine whether the model is accurate with respect to the available data. This article concerns cases where the model is admittedly accurate (with respect to the available data), but is subjectively considered objectionable. That is a totally different issue.