Would proponents of the integrated information theory of consciousness consider neural networks in machine learning to be conscious?
In integrated information theory consciousness is not only about amount of integration, but also how it is structure and dynamic activity. One of it's major goals is explaining the difference between awake, asleep and altered states of mind, where it is obvious that these are not simply properties possessed by a brain, but about dynamics.
So, a neutral network: IIT generates a spectrum for consciousness, instead of a binary of got it or not. In this picture, even a thermostat has a minimal amount of integrated information, as it works. Similarly a neural network in operation can.
We think a whole human brain can be simulated using neural networks, or at least The Human Brain Project do. If that is right, you question is a bit like asking 'Can a computer be intelligent?' - they can be, contextually if programmed right and depending how you define your terms.
Conscious is a big word. Generally what we mean is not the literal interpretation, 'aware of thing/s', but self-conscious, and not unconscious. We think there is a kind of feedback that occurs with having a model of ourselves in our minds, as manifested by the Mirror Test applied to different animal species, and as understood in Hofstadter's Strange Loops. In this sense, we haven't generated this kind of structure in neural networks, and we can't describe them as self-conscious. We can see the drive towards 'intelligible intelligence', deep-learning algorithms that can explain what they learned and how (see eg The challenge of crafting intelligible intelligence), can be understood as moving in this direction.