That just moves the decision point to “could be used by a police state”. Some people prefer humans as being the only people who can determine what a civil violation is. E.g. if a policeman doesn’t pull you over you shouldn’t be punished. This allows for great personal discretion and the potential for a police state through such discretionary exertion of power as exemplified by the famous line

> “For my friends, everything. For my enemies, the law”

I prefer speed cameras, red-light cameras, and so on because automated enforcement removes discretionary power from enforcement agents. I believe that enforcement agents are the arms of the civic body tasked with ensuring the commons are not ruined. Consequently, controlling the exertion of discretion is entirely an engineering approach to controlling quality of enforcement. Others believe that enforcement agents are our rulers or agents thereof that we must submit to - and therefore to whom we must give great discretion so that they can choose whether we are worthy or not.

That is incompatible with all being equal before the law.

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goda90 a day ago | parent [-]

> I prefer speed cameras, red-light cameras, and so on because automated enforcement removes discretionary power from enforcement agents.

There can still be corruption with automated enforcement. If the punishment is fees and the fees go towards the budget of those in charge of the automation parameters, then you'll see manipulation of parameters to increase fees paid. Shorter yellow lights, shorter distance between a speed limit sign and the speed camera, requiring more effort to contest the automation when it's wrong than to just pay the fee, etc.

	▲	arjie a day ago \| parent [-]
		This is true. A useful difference between this form of parameter optimization and the unalignability of humans is that it is possible to force constraints on these parameters. E.g. San Francisco’s speed cameras run in collection mode to obtain baselines and then in enforcement mode where they have to demonstrate reduction in speeding. California mandates minimum yellow times. Flaws exist but we can constraint-engineer to minimize adversarial optimization of parameters. Improvements allow us to asymptotically approach our goal optimum. Human discretion, on the other hand, is fluid and dodges all attempts at enforcement. We require many human agents solely because transforming unstructured vision space into structured judgment was impossible in the past. Currently, in a sufficiently constrained environment (roadways) we are able to solve sufficiently constrained problems (speeding, running a red light). One hopes that we are able to solve this problem in more spaces as time goes by.