Huh... of course I know definition of accuracy, and how its calculated. However even in supervised learning, accuracy is only used in very limited cases such a

Huh... of course I know definition of accuracy, and how its calculated. However even in supervised learning, accuracy is only used in very limited cases such as multi class classification. For a whole bunch of problems including the one being discussed its a poor and in some cases a biased metric. E.g. consider a heavily unbalanced problem 99% positive labels. By predicting all instances with majority label its possible to get 99% accuracy. There are several better metrics, False Accept rates, Precision Recall curves etc. Without knowing how the dataset was collected, did the "username" leaked into the dataset, etc. its impossible to evaluate such outlandish claims. The whole moral and ethical debate is non-sequitur, and harms legitimate deep learning research.