Student Blogger - Aggregating Probabilities across Offenses in Criminal Law
Professor Ariel Porat recently presented his paper (with Alon Harel), Aggregating Probabilities across Offenses in Criminal Law, at the Law and Economics Workshop. This is a forum where academic working papers are presented and discussed among interested faculty and students.
To be convicted for a criminal offense, it must be proved beyond a reasonable doubt that the defendant committed the offense. This currently remains true even when the defendant is charged with multiple offenses. He must be guilty beyond a reasonable doubt for each individual offense. As a result, some criminal defendants may remain unconvicted of any offense even though it is likely that the defendant committed each offense (but not beyond a reasonable doubt for any single offense), and almost certain that he committed at least one of the offenses (beyond a reasonable doubt).
Professor Ariel Porat argues that the probabilities for these individual offenses should be aggregated so that such defendants are convicted of some crime. The question should be whether it is beyond a reasonable doubt that the defendant committed an offense instead of whether it is beyond a reasonable doubt that a defendant committed a specific offense. This reformulation certainly would result in more criminals being convicted (increasing deterrance), but it would also increase the number of innocent people falsely convicted. The desirability of this approach hinges on minimizing the latter.
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