As the BeeToxAI data set was collected from many sources, including the scientific literature, anthropogenic biases could be present e.g., in the choices of which molecules to test for bee toxicity. As a result, the training and testing data distributions could differ, and the performance measure on the hold-out set (sampled from the training distribution) may not reflect the generalization error when the model is deployed. Ref. articulates various types of this dataset shift. Anthropogenic biases in data sets in chemistry have been uncovered and shown to cause machine learning algorithms trained on them to exhibit poorer generalization performance .