Applications of Density Estimation to Legal Theory

, I wrote an article in regards to the concept (and a few purposes!) of density estimation, and the way it’s a highly effective software for quite a lot of strategies in statistical evaluation. By overwhelmingly in style demand, I assumed it could be fascinating to make use of density estimation to derive some perception on some fascinating information — on this case, information associated to authorized concept.

Though it’s nice to dive deep into the mathematical particulars behind the statistical strategies to type a stable understanding behind the algorithm, on the finish of the day we wish to use these instruments to derive cool insights from information!

On this article, we’ll use density estimation to investigate some information relating to the impression of a two-verdict vs. a three-verdict system on the juror’s perceived confidence of their ultimate verdict.

Contents

Background & Dataset

Our authorized system within the US makes use of a two-option verdict system (responsible/not responsible) in prison trials. Nevertheless, another nations, particularly Scotland, use a three-verdict system (responsible/not responsible/not confirmed) to find out the destiny of a defendant. On this three-verdict system, jurors have the extra alternative to decide on a verdict of “not confirmed”, which signifies that the prosecution has delivered inadequate proof to find out whether or not the defendant is responsible or harmless.

Legally, the “not confirmed” and “not responsible” verdicts are equal, because the defendant is acquitted beneath both consequence. Nevertheless, the 2 verdicts carry completely different semantic meanings, as “not confirmed” is meant to be chosen by jurors when they aren’t satisfied that the defendant is culpable for or harmless from the crime at hand. 

Scotland has recently abolished this third verdict because of its complicated nature. Certainly, when studying about this myself, I came across conflicting definitions for this verdict — some sources outlined it as the choice to pick out when the juror believes that the defendant is culpable, however the prosecution has did not ship ample proof to convict them. This may increasingly give a defendant who has been acquitted by the “not confirmed” consequence an analogous stigma as a defendant who was discovered responsible within the eyes of the general public. In distinction, other sources outlined the decision as the center floor between responsible and innocence (complicated!).

On this article, we’ll analyze information containing the perceived confidence of verdicts from mock jurors beneath the two-option and three-option verdict system. The info additionally accommodates info relating to whether or not there was conflicting proof current within the testimony. These options will enable us to research whether or not the perceived confidence ranges of jurors of their ultimate verdicts differ relying on the decision system and/or the presence of conflicting proof.

For extra details about the info, try the doc.

Density Estimation for Exploratory Evaluation

With out additional ado, let’s dive into the info!

mock <- learn.csv("information/MockJurors.csv")
abstract(mock)

Our information consists of 104 observations and three variables of curiosity. Every remark corresponds to a mock juror’s verdict. The three variables we’re fascinated by are described under:

• verdict: whether or not the juror’s choice was made beneath the two-option or three-option verdict system.
• battle: whether or not conflicting testimonial proof was current within the trial.
• confidence: the juror’s diploma of confidence of their verdict on a scale from 0 to 1, the place 0/1 corresponds to low/excessive confidence, respectively.

Let’s take a short have a look at every of those particular person options.

# barplot of verdict
ggplot(mock, aes(x = verdict, fill = verdict)) + 
        geom_bar() +
      geom_text(stat = "depend", aes(label = after_stat(depend)), vjust = -0.5) +
      labs(title = "Depend of Verdicts") +
      theme(plot.title = element_text(hjust = 0.5))

# barplot of battle
ggplot(mock, aes(x = battle, fill = battle)) + 
         geom_bar() +
      geom_text(stat = "depend", aes(label = after_stat(depend)), vjust = -0.5) +
      labs(title = "Depend of Battle Ranges") +
      theme(plot.title = element_text(hjust = 0.5))

# crosstab: verdict & battle
# i.e. distribution of conflicting proof throughout verdict ranges
ggplot(mock, aes(x = verdict, fill = battle)) +
  geom_bar(place = "dodge") +
  geom_text(
    stat = "depend",
    aes(label = after_stat(depend)),
    place = position_dodge(width = 0.9),
    vjust = -0.5
  ) +
  labs(title = "Verdict and Battle") +
  theme(plot.title = element_text(hjust = 0.5))

The observations are evenly break up among the many verdict ranges (52/52) and practically evenly break up throughout the battle issue (53 no, 51 sure). Moreover, the distribution of battle seems to be evenly break up throughout each ranges of verdict i.e. there are roughly an equal variety of verdicts made beneath conflicting/no conflicting proof recorded for each verdict programs. Thus, we will proceed to check the distribution of confidence ranges throughout these teams with out worrying about imbalanced information affecting the standard of our distribution estimates.

Let’s have a look at the distribution of juror confidence ranges.

We will visualize the distribution of confidence ranges utilizing density estimates. Density estimates, can present a transparent, intuitive show of a variable’s distribution, particularly when working with giant quantities of knowledge. Nevertheless, the estimate might range significantly with respect to some parameters. For example, let’s have a look at the density estimates produced by varied bandwidth selection methods.

bws <- record("SJ", "ucv", "nrd", "nrd0")

# Arrange a 2x2 grid for plotting
par(mfrow = c(2, 2))  # 2 rows, 2 columns

for (bw in bws) {
  pdf_est <- density(mock$confidence, bw = bw, from = 0, to = 1) 
  
  # Plot PDF
  plot(pdf_est,
       foremost = paste("Density Estimate: Confidence (", bw, ")" ),
       xlab = "Confidence",
       ylab = "Density",
       col = "blue",
       lwd = 2)
  rug(mock$confidence)
  # polygon(pdf_est, col = rgb(0, 0, 1, 0.2), border = NA)
  grid()
}

# Reset plotting structure again to default (optionally available)
par(mfrow = c(1, 1))

The density estimates produced by the Sheather-Jones, unbiased cross-validation, and regular reference distribution strategies are pictured above.

Clearly, the selection of bandwidth may give us a really completely different image of the arrogance stage distribution.

• Utilizing unbiased cross-validation gives the look that the distribution of confidence may be very sparse, which isn’t stunning contemplating how small our dataset is (104 observations).
• The density estimates produced by the opposite bandwidths are pretty comparable. The estimates produced by the conventional reference distribution strategies seem like barely smoother than that produced by Sheather-Jones, for the reason that regular reference distribution strategies use the Gaussian kernel of their computation. Total, confidence ranges seem like extremely concentrated round values of 0.6 or larger, and its distribution seems to have a heavy left tail.

Now, let’s get into the fascinating half and look at how juror confidence ranges might change relying on the presence of conflicting proof and the decision system.

# plot distribution of Confidence by Battle
# use Sheather-Jones bandwidth for density estimate
ggplot(mock, aes(x = confidence, fill = battle)) +
  geom_density(alpha = 0.5, bw = bw.SJ(mock$confidence)) + 
  labs(title = paste("Density: Confidence by Battle")) + 
  xlab("Confidence") + 
  ylab("Density") +
  theme(plot.title = element_text(hjust = 0.5))

It seems that juror confidence ranges don’t differ a lot within the presence of conflicting proof, as proven by the massive overlap within the confidence density estimates above. Maybe within the presence of no conflicting proof, jurors could also be barely extra assured of their verdicts, because the confidence density estimate beneath no battle seems to point out greater focus of confidence values larger than 0.8 relative to the density estimate beneath the presence of conflicting proof. Nevertheless, the distributions seem practically the identical.

Let’s look at whether or not juror confidence ranges range throughout two-option vs. three-option verdict programs.

# plot distribution of Confidence by Verdict
# use Sheather-Jones bandwidth for density estimate
ggplot(mock, aes(x = confidence, fill = verdict)) +
  geom_density(alpha = 0.5, bw = bw.SJ(mock$confidence)) + 
  labs(title = paste("Density: Confidence by Verdict")) + 
  xlab("Confidence") + 
  ylab("Density") +
  theme(plot.title = element_text(hjust = 0.5))

This visible supplies extra compelling proof to counsel that confidence ranges are usually not identically distributed throughout the 2 verdict programs. It seems that jurors could also be barely much less assured of their verdicts beneath the two-option verdict system relative to the three-option system. That is supported by the truth that the distribution of confidence beneath the two-option and three-option verdict programs seem to peak round 0.625 and 0.875, respectively. Nevertheless, there may be nonetheless important overlap within the confidence distributions for each verdict programs, so we would wish to formally take a look at our declare to conclude whether or not confidence ranges differ considerably throughout these verdict programs.

Let’s look at whether or not the distribution of confidence differs throughout joint ranges of verdict and battle.

# plot distribution of Confidence by Battle & Verdict
# use Sheather-Jones bandwidth for density estimate
ggplot(mock, aes(x = confidence, fill = battle)) +
  geom_density(alpha = 0.5, bw = bw.SJ(mock$confidence)) +
  facet_wrap(~ verdict) +
  labs(title = paste("Density: Confidence by Battle & Verdict")) +
  xlab("Confidence") +
  ylab("Density") +
  theme(plot.title = element_text(hjust = 0.5))

Analyzing the distribution of confidence stratified by battle and verdict provides us some fascinating insights.

• Beneath the two-verdict system, confidence ranges of verdicts made beneath conflicting proof/no conflicting proof seem like very comparable. That’s, jurors appear to be equally assured of their verdicts within the face of conflicting proof when working beneath the standard responsible/not responsible judgement paradigm.
• In distinction, beneath the three-option verdict, jurors appear to be extra assured of their verdicts beneath no conflicting proof relative to when conflicting proof is current. Their corresponding density plots present that verdicts with no conflicting proof present a lot greater focus at excessive confidence ranges (confidence > 0.75) in comparison with verdicts made with conflicting proof. Moreover, there are practically no verdicts made beneath the absence of conflicting proof the place the jurors reported confidence ranges lower than 0.2. In distinction, within the presence of conflicting proof, there’s a a lot bigger focus of verdicts that had low confidence ranges (confidence < 0.25).

Formally Testing Distributional Variations

Our exploratory information evaluation confirmed that juror confidence ranges might differ relying on the decision system and whether or not there was conflicting proof. Let’s formally take a look at this by evaluating the confidence densities stratified by these components.

We’ll perform exams to check the distribution of confidence within the following settings (as we did above in a qualitative method):

• Distribution of confidence throughout ranges of battle.
• Distribution of confidence throughout ranges of verdict.
• Distribution of confidence throughout ranges of battle and verdict.

First, let’s evaluate the distribution of confidence within the presence of conflicting/no conflicting proof. We will evaluate these confidence distributions throughout these battle ranges utilizing the sm.density.compare() operate that’s offered as a part of the sm package deal. To hold out this take a look at, we will specify the next key parameters:

• x: vector of knowledge whose density we wish to mannequin. For our functions, this might be confidence.
• group: the issue over which to check the density of x. For this instance, this might be battle.
• mannequin: setting this to equal will conduct a speculation take a look at figuring out whether or not the distribution of confidence differs throughout ranges of battle.

Moreover, we are going to set up a standard bandwidth for the density estimates of confidence throughout the degrees of battle. We’ll do that by computing the Sheather-Jones bandwidth for the confidence ranges for every battle subgroup, then computing the harmonic imply of those bandwidths, after which set that to the bandwidth for our density comparability.

For all of our speculation exams under, we might be utilizing the usual α = 0.05 standards for statistical significance.

set.seed(123)

# outline subsets for battle
no_conflict <- subset(mock, battle=="no")
yes_conflict <- subset(mock, battle=="sure")

# compute Sheather-Jones bandwidth for subsets
bw_n <- bw.SJ(no_conflict$confidence)
bw_y <- bw.SJ(yes_conflict$confidence)
bw_h <- 2/((1/bw_n) + (1/bw_y)) # harmonic imply

# evaluate densities
sm.density.evaluate(x=mock$confidence, 
                   group=mock$battle, 
                   mannequin="equal", 
                   bw=bw_h, 
                   nboot=10000)

The output of our name to sm.density.evaluate() produces the p-value of the speculation take a look at talked about above, in addition to a graphical show overlaying the density curves of confidence throughout each ranges of battle. The big p-value (p=0.691) means that we’ve inadequate proof to reject the null speculation that the densities of confidence for battle/no-conflict are equal. In different phrases, this implies that jurors in our dataset are likely to have comparable confidence of their verdicts, no matter whether or not there was conflicting proof within the testimony.

Now, we’ll conduct an analogous evaluation to formally evaluate juror confidence ranges throughout each verdict programs.

set.seed(123)

# outline subsets for battle
two_verdict <- subset(mock, verdict=="two-option")
three_verdict <- subset(mock, verdict=="three-option")

# compute Sheather-Jones bandwidth for subsets
bw_2 <- bw.SJ(two_verdict$confidence)
bw_3 <- bw.SJ(three_verdict$confidence)
bw_h <- 2/((1/bw_2) + (1/bw_3)) # harmonic imply

# evaluate densities
sm.density.evaluate(mock$confidence, group=mock$verdict, mannequin="equal", 
                   bw=bw_h, nboot=10000)

We see that the p-value related to the comparability of confidence throughout the two-verdict vs. three-verdict system is way smaller (p=0.069). Though we nonetheless fail to reject the null speculation, a p-value of 0.069 on this context signifies that if the true distribution of confidence ranges was an identical for two-verdict and three-verdict programs, then there may be an roughly 7% probability that we come throughout empirical information the place the distribution of confidence throughout each verdict programs differs at the very least as a lot as what we see right here. In different phrases, our empirical information is pretty unlikely to happen if jurors have been equally assured of their verdicts throughout each verdict programs.

This conclusion aligns with what we noticed in our qualitative evaluation above, the place it appeared that the arrogance ranges for verdicts beneath the two-verdict vs. three-verdict system have been completely different — particularly, verdicts beneath the three-verdict system gave the impression to be made with greater confidence than verdicts made beneath two-verdict programs. 

Now, for the needs of future investigation, it might be nice to increase the info to incorporate the ultimate verdict choice (i.e. responsible/not responsible/not confirmed). Maybe, this extra information may assist make clear how jurors really see the “not confirmed” verdict.

• If we see greater confidence ranges within the “responsible”/“not responsible” verdicts beneath the three-verdict system relative to the two-verdict system, this may increasingly counsel that the “not-proven” verdict is successfully capturing the uncertainty behind the choice making of the jurors, and having it as a 3rd verdict supplies fascinating flexibility that two-option verdict system lacks.
• If the arrogance ranges within the “responsible”/“not responsible” verdicts are roughly equal throughout each verdict programs, and the arrogance ranges of all three verdicts are roughly equal within the three-verdict system, then this may increasingly counsel that the “not confirmed” verdict is serving as a real third possibility impartial of the standard binary verdicts. That’s, jurors are opting to decide on “not confirmed” primarily for causes apart from their uncertainty behind classifying the defendant as responsible/not responsible. Maybe, jurors view “not confirmed” as the decision to decide on when the prosecution has did not ship convincing proof, even when the juror has a touch of the true culpability of the defendant.

Lastly, let’s take a look at whether or not there are any variations within the distribution of confidence throughout completely different ranges of battle and verdict.

To check for variations within the distribution of confidence throughout these subgroups, we will run a Kruskal-Wallis test. The Kruskal-Wallis take a look at is a non-parametric statistical technique to check for variations within the distribution of a variable of curiosity throughout teams. It’s applicable while you wish to keep away from making assumptions in regards to the variable’s distribution (i.e. non-parametric), the variable is ordinal in nature, and the subgroups beneath comparability are impartial of one another. Basically, you could consider it because the non-parametric, multi-group model of a one-way ANOVA.

R makes this simple for us by way of the kruskal.test() API. We will specify the next parameters to hold out our take a look at:

• x: vector of knowledge whose distribution we wish to evaluate throughout teams. For our functions, this might be confidence.
• g: issue figuring out the teams over which we wish to evaluate the distribution of x. We’ll set this to group_combo, which accommodates the subgroups of verdict and battle.

kruskal.take a look at(x=mock$confidence, 
             g=mock$group_combo) # group_combo: subgroups outlined by verdict, battle

The output of the Kruskal-Wallis take a look at (p=0.189) means that we lack ample proof to say that juror confidence ranges differ throughout ranges of verdict and battle.

That is considerably sudden, as our qualitative evaluation appeared to counsel that partitioning every verdict group by battle segmented the confidence values in a significant method. It’s worthy to notice that there was a small quantity of knowledge in every of those subgroups (25-27 observations), so gathering extra information may very well be a subsequent step to research this additional.

Future Investigation & Wrap-up

Let’s briefly recap the outcomes of our evaluation: 

• Our exploratory information evaluation appeared to point that juror confidence ranges differed throughout verdict programs. Moreover, the presence of conflicting proof appeared to have an effect on juror confidence ranges within the three verdict system, however have little have an effect on within the two-verdict system. Nevertheless, none of our statistical exams offered important proof to assist these conclusions. 
• Though our statistical exams weren’t supportive, we shouldn’t be so fast to dismiss our qualitative evaluation. Subsequent steps for this investigation may embrace getting extra information, as we have been working with solely 104 observations. Moreover, extending our information to incorporate the decision choices of the jurors (responsible/not responsible/not confirmed) may allow additional investigation into when jurors choose to decide on the “not confirmed” verdict.

Thanks for studying! You probably have any extra ideas about how you’d’ve carried out this evaluation, I’d love to listen to it within the feedback. I’m definitely no area professional on authorized concept, so making use of statistical strategies on authorized information was a terrific studying expertise for me, and I’d love to listen to about different fascinating issues on the intersection of the 2 fields. Should you’re fascinated by studying additional, I extremely advocate trying out the sources under!

The writer has created all photographs on this article.

Sources

Information:

Authorized concept:

Statistics: