The human participant may consider multiple distinct machine results, each a point in the space of algorithm, data set, bias applied to the problem domain. Human intuition is injected into the process and the result will be greater than the sum of the machines and a lone human mind.

What is interesting to note, now that above idea is considered, is that this process model itself belongs to the set of human-machine coordinations. Another process model is where low level human mind is used to perform recognition tasks too hard (or too slow) for machine to perform, for example using porn surfers to perform computation tasks via e.g. captcha like puzzles.

Long term social ramifications of all this is also interesting to consider as it motivates machines to breed distinct types of humans ;)

I imagine you need the data science to discern semantically relevant from irrelevant signals. How else do you “tell” your model what to look for? You could easily train for an irrelevant but fitting model.