If Facebook lawyers understood the implications of LeCun's argument, they wouldn't be happy! There are two types of explanations here: (1) why did the data com

If Facebook lawyers understood the implications of LeCun's argument, they wouldn't be happy! There are two types of explanations here: (1) why did the data come to be as it is, (2) why did my ML make the prediction it did. Science looks for the answer to (1), and causal models are a great way to think about it. Science and engineering, when they go hand in hand, build a machine by saying "Here is data, let me do science to understand nature's underlying laws, and my machine shall be based on those laws". The machine is inherently explainable because it's based on scientific laws. In the ML world, we can bypass the "learn scientific laws" part, and jump straight to "build a machine based on data". So the best answer to (2) has got to be "my ML made the prediction it did because of its training data". As Pearl said, ML is just curve fitting, so the only way to "explain" a ML prediction is to say "here are the points that the curve was fitted to". Prediction is just reading a value off the curve. Think the machine is biased? Look for bias in the training dataset! Think the machine is inaccurate? Look for sparsity or conflict in the training dataset! So the consequence of LeCun's distinction is that, when the GDPR calls for explainability of ML decision making, it is really calling for sharing of the training data. Facebook, watch out!