English: Data annotation for AI is a subjective activity which involves a lot of interpretative work and communication between the annotators and the data scientists. Sometimes, when you are labeling a training dataset for AI, you bump into a lot of edge cases: occluded objects, blurry images, groups of many objects together, etc. How you annotate them can be crucial for ensuring that the resulting AI system is robust in cases of uncertainty. This is what we call the 'long tail' of AI edge cases and where human involvement is most needed.