[dragonwriter]

The problems are really twofold:

(1) Defining the proper goals, and

(2) Measuring the right things (such as the real goals of interest rather than biased proxies.)

With police deployments, you are assuming the solution (rather than letting your algorithm optimize it) by saying "I want to put more police where more arrests occur". What you really want is probably something more like (the exact goal may be different, of course) "I want to deploy police resources where it will most effectively reduce the incidence of crime, weighted by some assigned measure of severity." Then let your ML algorithm crunch the various measurable factors and produce an optimum deployment to do that.

(But, then again with that goal -- and similar problems exist with many likely real goals -- you run into the other problem, which is measuring the incidence of crime -- measuring crime reports may be the obvious approach, but there's plenty of evidence that lots of factors can bias crime reports, including communities having bad experience with police being less likely to report crimes.)

[wrsh07]

Thank you. This is so much clearer than what I was saying.

As you say, proper goals and measurement can fix a lot of these problems, and I don't think it's obvious that ml algorithms solve either of those