kybernetikos comments on Statistical Prediction Rules Out-Perform Expert Human Judgments - Less Wrong

68 Post author: lukeprog 18 January 2011 03:19AM

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Comment author: shokwave 18 January 2011 12:44:40PM *  0 points [-]

Because those are the class of problems this post discusses.

From the top of the post:

A parole board considers the release of a prisoner: Will he be violent again? A hiring officer considers a job candidate: Will she be a valuable asset to the company? A young couple considers marriage: Will they have a happy marriage?

The cached wisdom for making such high-stakes predictions is to have experts gather as much evidence as possible, weigh this evidence, and make a judgment. But 60 years of research has shown that in hundreds of cases, a simple formula called a statistical prediction rule (SPR) makes better predictions than leading experts do.

Comment author: kybernetikos 19 January 2011 05:32:18PM *  2 points [-]

A parole board considers the release of a prisoner: Will he be violent again?

I think this is the kind of question that Miller is talking about. Just because a system is correct more often, doesn't necessarily mean it's better.

For example if the human experts allowed more people out who went on to commit relatively minor violent offences and the SPRs do this less often, but are more likely to release prisoners who go on to commit murder then there would be legitimate discussion over whether the SPR is actually better.

I think this is exactly what he is talking about when he says

Where AI's compete well generally they beat trained humans fairly marginally on easy (or even most) cases, and then fail miserably at border or novel cases. This can make it dangerous to use them if the extreme failures are dangerous.

Whether or not there is evidence that says this is a real effect I don't know, but to address it what you really need to measure is total utility of outcomes rather than accuracy.

Comment author: Miller 19 January 2011 10:03:50PM 0 points [-]

Yes. You got it, exactly.