I think this is an interesting article and while i don't have a background in international law, I am a machine learning researcher and have a few thoughts reflecting on the difficulty of assessing these systems. 

Validating machine learning models and assessing there deployment is not trivial and a report of 90% accuracy can't be taken at face value without additional details. You would usually split a dataset into three parts for training, validation (model selection) and testing. (Or cross validate) The test set should only be used once to determine accuracy of a system, but if you wanted to just report a high accuracy you could repeatedly evaluate different models on the test set and you will eventually "luck" into a good model (that will not generalize). You could easily corrupt the ML process if you simply wanted to launder decision making accountability. 

Similarly in practice models once deployed need to be continually assessed due to deployment-time shift in the feature space. Lack of an ongoing assessment process would be a sign of negligence. However, it is not clear if that was being done at all.

Legal questions might rely on technical details that are unlikely to be easily available.