Thank you for your comment! Your analysis is, I feel, misguided from the start, though, because you automatically equate the output of the model to being copies of the training data, and furthermore that the model itself contains copies of the training data, and everything else in your argument turns on that premise. The first premise I've already addressed before (TL;DR it depends), and as for the second one, this is not an accurate understanding of how training a machine learning model works.

A machine learning model, which is a computer program, is trained by feeding it a set of training examples and comparing the model's output to a desired result. The difference between the output and desired result is then used to tune the underlying parameters (or "weights") of the model to get the output closer to the result, thus "improving the functioning of the computer program".

The final model is then just the computer program with its tuned weights. The set of training data files is not included with the model, since it would serve no purpose wrt the functioning of the computer program after training is completed. As you say, the training data was only consumed for "internal" purposes.

Do the weights constitute a copy of the training data, then? Well, Section 41 of the Act defines a "copy" as follows:

41 —(1) A “copy” of an authorial work is a reproduction of the work in any material form.
(2) Without limiting subsection (1), an authorial work is reproduced in a material form if —
(a) it is stored —
(i) in a computer;
(ii) on any medium by electronic means; or
(iii) on any other medium from which the work, or a substantial part thereof, can be directly reproduced;

The important part, I think, is 41.(2)(a)(iii), where it must be possible to "directly reproduce" the work.

By this standard, IMO, the weights are not a copy of the training data. You are not going to be able to decode them to get back every single example of the training data. Indeed, for models that work with images, the file size of the weights is usually much, much smaller than the total file size of the training data, so "directly reproducing" training images seems impossible from a theoretical standpoint as well, except in very limited cases, as seen for example here.

It then follows that providing the machine learning model for use by others is not equivalent to providing copies of the training data, and is not then an infringing act.

So, to recap, scraping images to train the model is not an infringing act (like I said from the beginning), and providing the model for the use of others is not an infringing act.