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.
False convictions would result from overestimating how independent multiple offenses are. If two crimes were completely independent and there was a 90% chance that the defendant is guilty of either offense, then there is a 99% chance (1 - 0.1*0.1) that he committed at least one of the crimes. However, the two offenses could also be completely dependent. For example, a defendant charged of robbing and murdering a victim likely either committed both crimes or wasn't involved at all. Thus, in this example it could be that there is a 90% chance that he is guilty of either offense, but there is also a 10% chance that he committed neither. A blind application of aggregate probabilities would overestimate (99% instead of 90%) how likely it is that the defendant committed either crime. If we assume that a 95% chance is the "beyond a reasonable doubt" threshold, then it would be erroneous to convict this defendant. Recognizing this problem, Porat suggests courts not aggregate probabilities when there is a large risk of interdependence--aggregate probabilities would not apply to the robbery/murder case. How feasible this would be remains to be seen.
Even with its difficulties and potential errors associated with interdependence, Porat argues that openly aggregating probabilities may be better than the current system. It seems reasonable that some judges and jurors may already knowingly or unknowingly connect offenses (aggregate) with one another and be determined to convict a defendant of something. Likewise, it's probably true that there are other judges and jurors who go to extreme lengths to separate the offenses. This leads to erratic application of the law. Open use of aggregate probabilities in criminal cases could conceivably lower error costs at the same time deterrance is increased.