Month: May 2012

Along with Bayesian reasoning, I think jury nullification is a topic that is important, yet widely unknown.  While somewhat controversial, I'll present my viewpoint and why I think it's important.  Before I start, I will note that I'm not a lawyer, and I'm limiting my discussion to criminal trials because the laws surrounding civil trials are different.

Borrowing from Wikipedia: "Jury nullification is a constitutional doctrine which allows juries to acquit criminal defendants who are technically guilty, but who do not deserve punishment. It occurs in a trial when a jury reaches a verdict contrary to the judge's instructions as to the law."  That is, a jury, though believing the defendant is guilty under the law, refuses to return a guilty verdict because they disagree with the law.

It certainly can be abused to allow criminals to go free who should be convicted, but I think far more important is that it can be used as another check and balance to protect the people from unjust laws.

What's particularly interesting is that jury nullification isn't something that can be outlawed without inherently jeopardizing the jury system at large.  What I mean is that jury nullification isn't a thing that was specifically created, it's more of a necessary side effect of having juries who can return verdicts without coercion.

In the United States, it is illegal to punish a jury member for the returned verdict.  I would hope that it is obvious why this law is necessary.  No jury could return an impartial verdict if they fear retribution.  Of course, there are laws against jury tampering as well, but the difference is that I could fear punishment without prior threat.  For instance, without this law one could be fired by one's employer after serving on a jury because one's employer didn't agree with the verdict.  For juries to even pretend to be impartial they can't be worrying about such consequences while deliberating.

In the United States, the other law necessary to create the opportunity for jury nullification is codified in the Fifth Amendment: "[N]or shall any person be subject for the same offence to be twice put in jeopardy of life or limb..."  Once a jury has returned a legitimate verdict the defendant can't be retried for the same crime.  Without this law a judge could simply continue issuing a new trial until the desired verdict was returned--effectively stripping the jury of any power.

With these two laws, jury nullification comes into existence as a possible avenue of justice and injustice.

Personally, I consider the benefits of jury nullification to outweigh the risks.  But that may be due to my predilection for the idea that it is better to let 10 guilty persons go free than to convict 1 innocent person.  I realize that many people feel instead that it is better to convict 10 innocent persons than allow 1 guilty person to go free, but I think that's rather sad (and perhaps a reason the U.S. has more prisoners than any other country in the world whether you measure per capita or absolute numbers).

The risk of allowing jury nullification is that like-minded individuals can allow criminal behavior by refusing to find defendants guilty when tried.  For example, an all-white jury of racists may refuse to convict a white person of murdering a black person.  This is clearly a bad thing and a failure of the justice system.  Yet this is entirely possible and legal because of the concept of jury nullification.

The benefit of allowing jury nullification is that like-minded individuals can protect themselves from an oppressive government.  For example, were the government to enact a law making it illegal to refuse a body search at airport security a jury could simply refuse to convict anyone tried under that law.  (What I find interesting is that this currently is law, yet the people that have broken it have never gone to trial.  I think the prosecutors are afraid jurors might refuse to convict and then airport security will look even stupider than they already do.  The most they've ever done is threaten a civil suit, which don't "suffer" from jury nullification because in civil suits the judge can override the jury.)

Of course, jury nullification only works if citizens are actually given jury trials for their crimes.  So when the government reclassifies you from citizen to terrorist and hands you over to the military for indefinite detention without a trial you're kind of out of luck.  I see this as a flaw rather than a feature.

We watched a movie last week on Netflix called Turtle: The Incredible Journey.


A. It was awesome.

B. I don't know what Heather finds to cry about. She didn't have to break out of an egg and then dig through sand for 3 days to get to the surface. She didn't have to then make a mad dash to the ocean, and she's certainly never been chased by crabs or birds who want to eat her. Her life is so easy!

Bayesian reasoning (the application of Bayes' theorem) is incredibly important, but virtually unknown (and not understood) in the general population.  While politicians, advertisers, and salespersons take advantage of this lack of understanding to extract votes and money; doctors, lawyers, and engineers can make life-threatening mistakes if they don't apply the process correctly.  It's a concept so vital to the proper interpretation of the world around us that I believe it should be a mandatory subject during high school.

Let's start with an example to motivate the subject.  Let's say you're not feeling well and you go to your doctor.  Your doctor can't determine what's wrong and decides to run some blood tests.  The results come back and your doctor informs you that the test for a terminal disease came back positive.  Do you panic?  Probably.  Should you panic? Not necessarily.  There is vital information which we don't know and need to know in order to understand the situation properly.

First, we need to know how accurate the test is.  We need to know the rates of type I and type II errors ("false positive" and "false negative", respectively).  Let's say you ask this question and your doctor tells you the test has a false positive rate of 1 in 100,000 and a false negative rate of 1 in 200,000.  One might believe that there is a 99.999% chance that you have this rare disease, but we need to interpret these error rates correctly.  This is where Bayes' theorem comes in and to apply it we also need to know how rare the disease is.

Let's say it's an extremely rare disease, affecting 1 in 200 million people.  Knowing this you can update the probability that you have the disease to something more accurate.  Applying Bayes' theorem we can calculate that a more accurate probability of having the disease is 4.8%.  That's a pretty big difference.

There is a caveat though; we're ignoring any compounding factors that led the doctor to run the test in the first place.  This calculation assumes that we had no particular reason to run the test, so we use the occurrence rate in the general population (1 in 200 million) in our calculation.  If you have specific risk factors that narrows the base group then the probability of having the disease will increase.

For example: let's suppose that the doctor chose to run this test specifically because you have high cholesterol levels and a family history of diabetes.  Suppose that the disease occurs in roughly 1 in 1 million of people with those risk factors.  Now, instead of using the 1 in 200 million as our occurrence rate, we'd use the 1 in 1 million.  In which case the probability of having the disease rises to 9.1%.

Suppose another risk factor increases the likelihood from 1 in 1 million to 1 in 100.  Now the probability of having the disease shoots up to 99.9%.  It's really important to understand what a positive result from a medical test actually means.  If you don't understand Bayes' theorem then you can wildly misinterpret reality and make some pretty serious mistakes.

Let's do another example.  This one is a little easier to understand than the medical testing example.

When I bought my car in 2007, the dealership tried to tack on a charge for something which amounted to a small insurance policy which would pay out if the car were stolen.  The argument used to push this charge was that Honda Civics are the number one stolen car.  Of course, our questions are:  Is that true?  And does it mean what we think it means?

According to the Insurance Bureau of Canada's 2006 list of the top ten most-stolen cars, the 2000, 1999, 1996 and 1994 Honda Civics took 4 of the 10 places.  Hmmm...sounds bad, huh?  But I'm sure you can guess by now that there's a catch.

It's vitally important to our analysis to know how many 2000, 1999, 1996, and 1994 Honda Civics exist in the first place and how many actually got stolen.  But we don't have any of these numbers.  What we have is someone taking a list of stolen cars and adding up each type and declaring the Honda Civic as the most stolen car.  We need to know how many are stolen compared to how many of them exist.

Luckily for us, the Highway Loss Data Institute understands the difference and correctly reports the likelihood of a vehicle getting stolen by comparing the "'theft claim frequency,' which is the number of thefts reported for every 1,000 of each vehicle on the road."  When we look at these numbers the picture changes entirely.  This top-ten list is filled with expensive cars like the Cadillac Escalade and several high-end pickup trucks.  The Honda Civic is nowhere to be found. 

If you don't understand Bayes' Theorem you can be manipulated into making bad decisions.  This applies all over the place in our lives.  It applies in our airport security procedures, our medical exams, our insurance decisions, political decisions, and our general level of fear about life.

You can read more about Bayes' Theorem in its Wikipedia article.  I'm not going to try to teach it here (unless I hear a demand for it in the comments) because it's not an entirely intuitive concept and it's a little tricky to wrap your head around (which is why we get it wrong so often).