It doesn't matter if it's only for complementing. Census data is supposed to be the source data, if you start to mingle and add some model in order to "enhance" your source data, you're tainting it with wrong samples. Then from there everything is biased.
My point is that there is a hype around AI that makes it look that it can do things that it can't. What they do in this paper is equivalent to using a correlation matrix, and a bunch of observations to generate a completely random population that matches what we input in there (ok, the neural net may find some non-linear relationships, but you get the idea). Yes, there is a significant correlation between car ownership and demographics, so the results do look good from far away, but the reality is that they will only map the car ownership factors on to other traits, and in terms of information, it is way poorer than actually doing a census.
My point is that there is a hype around AI that makes it look that it can do things that it can't. What they do in this paper is equivalent to using a correlation matrix, and a bunch of observations to generate a completely random population that matches what we input in there (ok, the neural net may find some non-linear relationships, but you get the idea). Yes, there is a significant correlation between car ownership and demographics, so the results do look good from far away, but the reality is that they will only map the car ownership factors on to other traits, and in terms of information, it is way poorer than actually doing a census.