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The status of death-sentenced defendants and the occurrence of exonerations, by time from conviction. The black line represents the total number of all death-sentenced defendants by time from conviction and the gray line the number of defendants who remained on death row DR and were therefore available for exoneration under threat of execution by time from conviction. The three areas between the black and gray lines display the dispositions of those defendants who were removed from death row over the time period by mode of removal: execution, suicide or death from natural causes, and legal proceedings court orders or executive clemency.

A minority of defendants who were removed from death by legal proceedings were exonerated. The plus symbols mark exonerations by date measured in time from conviction. The 10 blue plus symbols on the black line mark exonerations that were not under threat of execution by the date of the completion of the exoneration. The red plus symbols on the gray line mark exonerations that were initiated under threat of execution by the date of removal of the defendants from death row. To estimate this cumulative probability, we use survival analysis.

This technique has been used in a related context, to estimate the rate of all reversals of death sentences in the United States It is most commonly used, however, to evaluate the efficacy of medical treatments when not all patients experience the outcome of interest. The issue we address is analogous, but the analogy is counterintuitive. We use survival analysis to assess the prospects of members of a population that is subject to a special risk. In biomedical survival studies, that terminal event that is studied is death from the pathology in question; for our study it is exoneration.

Survival analysis is often used to evaluate the efficacy of a medical treatment that may reduce mortality from a pathology. This too is a counterintuitive analogy. Our focus, however, is not on the treated group those removed from death row but on those who remain untreated defendants who remain under threat of execution and therefore at high risk of exoneration.

In this study, as in medical research, subjects may be removed from the population of interest by means other than the terminal event at issue. In survival analysis of a disease, the usual means of exit by other means are death from a different cause or discontinuation of participation in the study.

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In our study, all deaths after capital sentencing by execution, suicide, or natural causes remove the person from the population that is subject to the risk of execution. A primary difficulty in estimating the cumulative probability of exoneration is that some defendants were censored, i. Some defendants were removed from that threat during the study period but would have been exonerated had they remained under threat; others, who were sentenced to death relatively recently, remained under threat and had not been exonerated at the end of the study period but would have been exonerated at some later point if the study period were extended.

As a result, a simple proportion of exonerated defendants to all defendants is a biased estimate of the cumulative probability of exoneration. We therefore use the Kaplan—Meier estimator to calculate the cumulative probability of exoneration under threat of execution for death-sentenced defendants, by time from conviction through This estimator takes account of the censoring of observations caused by recency of incarceration on death row, death from suicide or natural causes, or other removals from the threat of execution.

The Kaplan—Meier survival function estimates the probability of being event-free remaining on death row up to a given length of time from conviction. Its complement 1 minus the estimator estimates the cumulative incidence of the event exoneration up to the given length of time from conviction. Unlike a simple proportion, the Kaplan—Meier estimator is unbiased in the presence of independent censoring see further discussions in Sensitivity Analysis , and is completely nonparametric; it can be viewed as a censored data analog of the empirical distribution function.

As Fig. Both results are virtually indistinguishable SI Materials and Methods , section 3. Exoneration under threat of execution is defined as exoneration that resulted from legal proceedings that were initiated before the end of and while the defendant was under sentence of death. This 4. To rely on this estimate, however, two additional steps are necessary.

An important assumption for the validity of the Kaplan—Meier estimator is that censoring events that remove subjects from consideration are statistically independent of the time to the event of interest if the subjects had not been removed. In this context, that assumption is plausible with respect to censoring by recency of conviction and by death from suicide or natural causes while under threat of execution. On the other hand, there are strong reasons to believe that both execution and removal from death row by legal procedures without exoneration are not independent of time-to-exoneration.

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Because the assumption of independence may be violated, sensitivity analysis is necessary. Some executed defendants may have been innocent, and, although none has been exonerated after execution 9 , they might have been exonerated if they had remained alive and on death row. However, we expect that the proportion of innocent defendants is lower among those who are executed than among those who remain on death row 7 SI Materials and Methods , section 4.

The threat of execution is the engine that drives the process of exonerating innocent death row prisoners, and it is likely that this process becomes more painstaking as inmates approach their execution dates. Courts and executive officials explicitly recognize that it is appropriate to take the possibility of innocence into account in deciding whether to reverse a conviction for procedural error or commute a death sentence to life imprisonment, and a wealth of anecdotal evidence suggests that this practice is widespread SI Materials and Methods , section 4.

As a result, those who are resentenced to punishments less than death are more likely to be innocent than those who remain on death row. In short, we believe that i executed defendants are less likely to have been exonerated if they had remained on death row than those who in fact remained on death row, and ii defendants who were removed from death row but remained in prison are more likely to have been exonerated if they had remained under threat of execution.

These two biases are not equivalent in magnitude. Nearly three times as many unexonerated death-sentenced defendants were resentenced to prison 2, as were executed Even a modest increase in the proportion of innocent defendants among death-sentenced prisoners resentenced to life imprisonment, compared with those who remain on death row, would more than offset a complete absence of innocent defendants among those who are executed.

We use competing risks methodology 18 , along with explicit assumptions about the counterfactual probability of exoneration for those who were executed or resentenced to prison, to develop a sensitivity analysis for the Kaplan—Meier estimate of the cumulative exoneration rate. First, we estimate the cumulative incidence of exoneration subject to the competing risks of execution and resentencing by The estimates of the probabilities of removal from risk of exoneration by exoneration under threat of execution, by execution itself, or by resentencing, are 2. Thus, a defendant sentenced to death had an estimated 2.

Consider instead the assumption that, had they remained on death row, i those who were executed would have had zero chance of exoneration, and ii those who were resentenced would have had twice the chance of exoneration as the entire population of defendants sentenced to death. This yields the following estimate of the cumulative probability of exoneration, had those who were exonerated or resentenced instead remained on death row: 2.

Using the Delta method, the confidence interval for this estimate is 3. A zero probability of exoneration for executed defendants had they remained on death row is necessarily, for the purposes of this estimate, a conservative assumption.

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We believe that the assumed probability of exoneration for those who were removed from death row and resentenced to prison, twice the mean for the population, is reasonable. We conclude that the Kaplan—Meier estimate we obtained is conservative. Indeed the same result we would obtain if we assume that the probability of exoneration for those resentenced to prison, had they remained on death row, is equal to or greater than 1. Because there is no general method to accurately determine innocence in a criminal case, we use a proxy, exoneration: an official determination that a convicted defendant is no longer legally culpable for the crime for which he was condemned.

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There will be misclassifications. Some exonerated defendants are guilty of the crimes for which they were sentenced to death. To date, one such case has come to light, and has been reclassified On the other side, some innocent defendants who remained on death row for more than Some may still be exonerated; some may be executed; and most will likely die in prison, on death row or off, of natural causes or suicide.

In the absence of better data we assume that the probability of a legal campaign to exonerate any prisoner under threat of death who has a plausible innocence claim is 1, and we assume that the probability of success for an innocent prisoner who remains under such threat for at least These are necessarily conservative assumptions. To the extent that these probabilities are in fact less than 1, our estimate will understate the actual rate of false convictions.

The distribution of possible misclassifications is asymmetrical: defendants remained on death row longer than Unless the process of death row exoneration is assumed to be unrealistically thorough, it is likely that the number of innocent death-sentenced defendants misclassified as guilty exceeds the number of guilty defendants exonerated under threat of execution and misclassified as innocent.

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Taken together, the sensitivity analysis and the likely net effects of misclassification both point in the same direction and suggest that our 4. We present a conservative estimate of the proportion of erroneous convictions of defendants sentenced to death in the United States from through , 4. This is a unique finding; there are no other reliable estimates of the rate of false conviction in any context. The main source of potential bias is the accuracy of our classification of cases as true or false convictions.

On that issue it is likely that we have an undercount, that there are more innocent death row defendants who have not been identified and exonerated than guilty ones who have been exonerated in error.

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The most charged question in this area is different: How many innocent defendants have been put to death 6? We cannot estimate that number directly but we believe it is comparatively low. If the rate were the same as our estimate for false death sentences, the number of innocents executed in the United States in the past 35 y would be more than 50 We do not believe that has happened. Our data and the experience of practitioners in the field both indicate that the criminal justice system goes to far greater lengths to avoid executing innocent defendants than to prevent them from remaining in prison indefinitely.

However, no process of removing potentially innocent defendants from the execution queue can be foolproof. It is possible that the death-sentencing rate of innocent defendants has changed over time. No specific evidence points in that direction, but the number and the distribution of death sentences have changed dramatically in the past 15 y One change, however, is unlikely to have much impact: the advent of DNA identification technology. DNA evidence is useful primarily in rape rather than homicide investigations. Unfortunately, we cannot generalize from our findings on death sentences to the rate of false convictions in any broader category of crime.

Capital prosecutions, and to a lesser extent murder cases in general, are handled very differently from other criminal cases.

How an ex-FBI profiler helped put an innocent man behind bars

There are theoretical reasons to believe that the rate of false conviction may be higher for murders in general, and for capital murders in particular, than for other felony convictions, primarily because the authorities are more likely to pursue difficult cases with weak evidence of guilt if one or more people have been killed However, there are no data that confirm or refute this hypothesis. There is very little scientific research testing the reliability of profiling, and the few existing studies have led to sharp disagreements over whether profilers can better predict the characteristics of criminals than nonprofilers.

Still, the public holds an outsized view of what profilers can do, said David Wilson, a professor of criminology at Birmingham City University in England who has written critically about profiling. Judges do, however, often allow profilers to testify about crime scene evidence and what it reveals about possible motives, modus operandi or links to similar crimes by a common perpetrator. We want to believe in that [Sherlock] Holmesian figure that can turn up and magically solve the crime. Edward J. Jennings wipes his hand over his brow — a small gesture that Safarik assigns great weight.

Toward the end of the episode, jarring piano music pulses in the background as Safarik offers a monologue about Jennings. Ultimately, he was responsible for it. Ehrlich watched the show in frustration. The lawyer hired another profiler, Peter Klismet Jr. As for her slightly lowered tube top, Klismet suggests many possible explanations.

Perhaps she was in the process of changing into new clothes, as she was headed to a night college class, he said, or maybe her top slid down as she lifted her hands in defense after seeing a gun. Ehrlich reached out to Safarik directly, saying there were serious concerns with the case and urging him to reconsider his assessment.