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Tim O'Reilly has advocated for the idea of algorithmic regulation - reducing the role of people and replacing them with automated systems in order to make goverment policy less biased and more efficient. But the idea has been criticized as utopianism, where actual implementations are likely to make government more opaque and even less responsive to the citizens who have the least say in the operation of society.
Now, as part of New America's annual conference What Drives Innovation Around the Country? Virginia Eubanks has written an essay examining such automation in the cases of pre-crime and welfare fraud. Is it possible to automate away human judgment from the inherently human task of governance and still achieve humane results? Or is inefficiency and waste an unavoidable part of the process?
(Score: 3, Interesting) by VLM on Sunday May 03 2015, @12:00PM
Wouldn't the legal analogy of algorithmic regulation be criminal justice mandatory minimums, which virtually everyone other than "get tough on crime" politicians sees as a BS idea?
This analogy serves two purposes:
1) This is how we get stuck with cruddy systems and stupid mandatory minimums have been around longer than the examples of algorithmic regulations.
2) If by some miracle we can fix the algorithmic regulation disease (good luck with that) then the same technique could be applied to eliminate mandatory minimum policies, which would be good.
Another obvious analogy with pre-crime algo-reg is a credit score.
A stretch of analogy would be IQ tests and other standardized tests as a gatekeeper to vocational training (or even education) and the resulting socioeconomic outcomes.
(Score: 0) by Anonymous Coward on Sunday May 03 2015, @12:45PM
It is worse than mandatory minimums because at least those are transparent.
One of the biggest problems with algorithmic regulation is that it is opaque. It spits out a result but we don't know why. In the best possible version each result would also come with a full explanation of how that result was produced. But when it is Big Data based using inferences and correlations such an explanation isn't likely to be meaningful and it brings that old problem we all know: "correlation is not causation."
Correlation might be sufficient for insurance underwriters, but when it comes to public policy it has the strong risk of reinforcing the status quo which would be great if everything was perfect and the world was a static place.