As long as they retain no data which could specifically identify the original person, yes. There is nothing wrong with building segmentation models as long as they aren't specific enough to identify a specific person.
My concern would be, how granular is too granular? What if we added "and live in zip code 12355 and is registered Green Party"? This now gets eerily specific, and might be sufficient to identify an individual.
Why would they ever discard that? Why would there be a granularity where ML suddenly stops working? Why would you even stop at one model per person, instead of one model per mood, or modes of thought at different stress points?
In fact they would desire that granularity most of all so as to reconcile the past and future state psychographic profiles for an individual- then they could attempt to isolate the causation of a state change- basically they need to identify the moment an individuals profile reflects the change from democrat to republican or vice versa. Or Religious to atheist etc.
My concern would be, how granular is too granular? What if we added "and live in zip code 12355 and is registered Green Party"? This now gets eerily specific, and might be sufficient to identify an individual.