No, accuracy is but one possible goal. "Machine learning practitioners take pride in models that optimize the value of that model." And since we cannot define

No, accuracy is but one possible goal. "Machine learning practitioners take pride in models that optimize the value of that model." And since we cannot define exactly what this value is (accuracy, RMSE, bias or a specific confusion matrix), we have to make a more abstract definition. We are now left with a question. Is this abstract definition quantifiable? If so, we can still exclude the statistician w.r.t. understandably. However, if we allow qualitative value as well, the traditional statistician is back at the table. Now, a further refinement needs to be made. Since fairness, accountability and transparency in Data Science are in the limelight, we can make a point that our ability to understand is a key metric for Machine Learning models as well. It is interesting to see the tendency to associate words or phrases to key neurons in embeddings. For example, in embeddings of faces, we can associate gender, ethnicitity, fatness and gaze with some of the embedded neurons.