NN training is deterministic (enough)

In my understanding, training an neural net is deterministic in the sense that matters for reproducibility. If you train the same model architecture in the same environment with the same data, you'll get the same final weights. This is true even if you draw from random distributions during training, as long as you choose the same seed(s) for the PRNG.

The input-output mapping of the trained model is usually also deterministic, except for some special-purpose stochastic models. Even those you may be able to make reproducible by fixing the PRNG seed, as above.

> The trained neural net (biological or digital) is its own qualia, which exists independent of the influences that trained it and it owes nothing to those influences.

It is not true that the weights "owe nothing" to the training data. As mentioned above, for a fixed PRNG seed, they are in fact a very complex closed-form function of that data - certainly "dependent" in the probability sense.

> By way of analogy - demanding a full accounting of the training material to satisfy an openness requirement is like demanding that you provide a full accounting of everything you were exposed to since birth before we allow you to operate freely in open society.

I think it's reasonable to have different expectations for software tools and the people that use them, and honestly kind of absurd not to.

> The very idea is absurd.

For humans, certainly. One key difference between humans and NNs here is that NNs have a thing called "training" with well-defined inputs and output, in a consistent and well-defined format, which makes enumerating the training data entirely feasible.

> We cannot possibly know everything that is going on in your head

But we can know everything that is going on inside a NN, although we may not be able to interpret it with respect to the inputs and outputs.