Reproducibility does not necessarily mean that other people should be able to reproduce the results in question (which would depend upon them knowing the relevant information in the first place) - but that an experiment can be replicated.
I would agree that publishing results is better for several reasons (ethics, pragmatism, probably more thorough evaluation, etc.), but the definition doesn't really deal with the public at all.
It doesn't have to be "arbitrarily many people". Just people other than the original scientist. So colleagues at Apple still qualify.
There are tons of secret research behind any company (and governments), and it's still "science".
(And conversely, lots of published scientific papers are actually not reproducible, but for most of them nobody bothers -- even if other scientists cite them as accurate).
It's just the conclusions that need to be reproducible. In fact, science as a whole works better when researchers devise alternate experiments to test the same conclusion.
For example, say I measure gravitational acceleration on Earth by dropping a feather repeatedly. I get a number and I publish it. If other scientists just repeat my exact experiment, they will get a similar number and my result will be confirmed. Yay science!
But if scientists decide to check my number by dropping a wide variety of other objects, they will illustrate the flaws in my original experiment and everyone will get a clearer picture of gravity.
So it's not about just reproducing an experiment; scientists seek to test and expand upon each others' results.
People studying machine learning are not like people running a psychology experiment. If you give the same code, data and computers to two different teams, they will get the same results, and they will understand why that happens.
That's because a machine learning system has been built up from known principles and hardware; whereas the human brain is not fully understood, so researchers must work their way down from observed evidence to induce theories.
What is a bug, anyway? It means the system is not working properly, which implies that folks know in advance how the system is intended to work.
When physicists were struggling to explain radiation, they didn't call it a bug in the atom. It was their understanding, not the system they studied, that needed fixing.