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Researchers worldwide have extensively explored the factors contributing to wrongful convictions and the characteristics of individuals affected by these miscarriages of justice for over a century. Despite these global efforts, limited research has been conducted on this issue in Spain. This study seeks to address this gap. We trained coders to assess available review judgments issued by the Spanish Supreme Court from 1996 to 2022. We identified 89 cases of individuals wrongly sentenced to deprivation of liberty. Our findings indicated that 92% of those wrongfully convicted were male, with the majority having a prior criminal record. Most exonerations involved minor crimes, and 85% of individuals were sentenced to less than 4.5 years of deprivation of liberty. Professional misconduct emerged as the primary contributing factor, followed by the misapplication of forensic science, misidentifications, false testimonies, and false confessions. This project sheds light on wrongful convictions in Spain, emphasizing the need for comprehensive measures to address this issue. The current results have practical implications for justice professionals, policymakers, and legal practitioners. It is crucial to educate professionals in the judicial system on the causes of judicial errors, the biases that may influence them, and best practices to improve processes and reduce the occurrence of wrongful convictions.

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Citing IP data, Johnson (2021), reported that sexual assault/rape was the most common offense associated with exoneration in the US. Also, stranger rape accounted for 72% of the entire IP database. To further examine the role of sexual assault, the current study examined all exonerations in Texas, the US state with the most sexual assault exonerations. Using NRE data, descriptive analyses, and reclassifying sexual assaults, we find drug offenses are the most common crime type associated with exonerations in Texas but sexual assault/rape accounts for a significant portion of Texas exonerations. Contrary to a common assumption, we also find that exculpatory DNA does not explain the substantial proportion of sexual assaults among exonerations. We also examine the role of stranger rape misidentification, youthful complainant recantations (perjury/false allegations) and false guilty pleas in the NRE Texas database. Finally, we discuss other patterns within the Texas exonerations and policy implications.

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When someone dies unexpectedly, a medical examiner may perform an autopsy to determine how they died (i.e., manner of death). Recent studies found that cognitive bias can affect manner of death judgments, such that extraneous non-medical information may cause the same death to be judged as either a homicide or accident, which has significant legal ramifications. In response, leading medical examiners clarified that manner of death is “not scientific” and “often does not fit well in court.” Yet medical examiners often testify in court, and little is known about how fact-finders appraise their judgments. To address this gap, we conducted two experiments in which mock jurors read and evaluated a medical examiner’s testimony at a murder trial (modeled after the real-world case of Melissa Lucio), while varying the expert’s opinion (i.e., homicide or accident) and the defendant’s attributes (i.e., an affluent white or underprivileged Latina woman). Overall, participants rated the medical examiner’s testimony as highly scientific, credible, and convincing, and it strongly affected their verdicts and belief in the defendant’s guilt, irrespective of the defendant’s attributes. Moreover, participants unexpectedly rated the expert as even more credible if they ruled the death a homicide rather than an accident. Our data thus reveal a worrisome disconnect between how medical examiners characterize their judgments (i.e., as nonscientific and tentative) and how jurors appraise those judgments (i.e., as highly scientific and practically dispositive). We discuss ways to remedy this disconnect, including reforming death investigation practices to curtail bias and encourage standardization and transparency.

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The purpose of this study is to examine the impact of race and ethnicity on time-to-exoneration through the lens of focal concerns theory. Focal concerns theory has been used to demonstrate that criminal justice actors are influenced by legal and extralegal factors in decision making and rely on stereotypes to assess blameworthiness, protection of the community, and in navigating practical constraints and consequences. Utilizing data obtained from the National Registry of Exonerations (N =507) survival analysis was performed. The findings indicate that Black exonerees experienced a longer time-to-exoneration than did White exonerees and that Hispanic exonerees experienced the shortest time-to-exoneration of all. The findings offer support for focal concerns theory in the demonstration that racial and ethnic differences are present in time-to-exoneration resulting in disparities which disadvantage minorities. Further support for focal concerns theory is found in that the legal components of a case are shown to be associated with racial and ethnic differences in time-to-exoneration.

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How might data analytic tools support intake decisions? When faced with a request for post-conviction assistance, innocence organizations’ intake staff must determine (1) whether the applicant can be shown to be factually innocent, and (2) whether the organization has the resources to help. These difficult categorization decisions are often made with incomplete information (Weintraub, 2022). We explore data from the National Registry of Exonerations (NRE; 4/26/2023, N = 3,284 exonerations) to inform such decisions, using patterns of features associated with successful prior cases. We first reproduce Berube et al. (2023)’s latent class analysis, identifying four underlying categories across cases. We then apply a second technique to increase transparency, decision tree analysis (WEKA, Frank et al., 2013). Decision trees can decompose complex patterns of data into ordered flows of variables, with the potential to guide intermediate steps that could be tailored to the particular organization’s limitations, areas of expertise, and resources.

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The recent advent of commercial artificial intelligence (AI), especially in natural language processing (NLP), introduces transformative possibilities for wrongful conviction research. NLP, a pivotal branch of AI that forms the basis for Large Language Models (LLMs), enables computers to interpret human language with a nuanced understanding. This technological advancement is particularly valuable for analyzing the complex language found in case documents associated with wrongful convictions. This paper explores the effectiveness of LLMs in analyzing and extracting data from case documents collected by the Innocence Project New Orleans and the National Registry of Exonerations. The diverse and comprehensive nature of these datasets makes them ideal for assessing the capabilities of LLMs. The findings of this study advance our understanding of how LLMs can be utilized to make wrongful conviction case documents easily accessible by automating the extraction of relevant data.

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In most U.S. jurisdictions, prosecutors are not required to clearly establish a reasonable basis for guilt prior to offering defendants plea deals. We apply Bayesian analyses, which are uniquely suited to illuminate the impact of prior probability of guilt on the informativeness of a particular outcome (i.e., a guilty plea), to demonstrate the risks of plea offers that precede evidence. Our primary prediction was that lower prior probabilities of guilt would coincide with a significantly higher risk for false guilty pleas. We incorporated data from Wilford, Sutherland et al. (2021) into a Bayesian analysis allowing us to model the expected diagnosticity of plea acceptance across the full range of prior probability of guilt. Our analysis indicated that, as predicted, when plea offers are accepted at lower prior probabilities of guilt, the probability that a plea is actually false is significantly higher than when prior probabilities of guilt are higher. In other words, there is a trade-off between prior probability of guilt and information gain. For instance, in our analysis, when prior probability of guilt was 50%, posterior probability of guilt (after a plea) was 77.8%; when prior probability of guilt was 80%, posterior probability of guilt was 93.3%. Our results clearly indicate the importance of ensuring that there is a reasonable basis for guilt before a plea deal is extended. In the absence of shared discovery, no such reasonable basis can be established. Further, these results illustrate the additional insights gained from applying a Bayesian approach to plea-decision contexts.