I think it can be done. Just maintain the data not in a standard tabular format but a time based graph instead. And put constraints to not look back. Constrained deep learning is a thing too.
The issue is that the machine has no causal model of how the predictions are leading to the data. You could, as you say, try to single-out some variables and rig how they're processed.
Here I dont think that helps: so long as you predict area A has more crime, area A is more policed and thus always appears to have more crime.
The issue, in my view, is not the data nor the algorithm -- no modification to either can fix the issue. The issue is the machine isn't embedded in the world, and esp. has no ability to acquire a rich understanding of the human social environment.
A fundamental aspect of understanding X is knowing what is irrelevant to X, ie., what "data" it is permissible/essential to ignore. In the case of crime, one ought ignore data from over-policing -- but this is not an effect which is present in the data itself.
Here I dont think that helps: so long as you predict area A has more crime, area A is more policed and thus always appears to have more crime.
The issue, in my view, is not the data nor the algorithm -- no modification to either can fix the issue. The issue is the machine isn't embedded in the world, and esp. has no ability to acquire a rich understanding of the human social environment.
A fundamental aspect of understanding X is knowing what is irrelevant to X, ie., what "data" it is permissible/essential to ignore. In the case of crime, one ought ignore data from over-policing -- but this is not an effect which is present in the data itself.