Matching Items (11)
Description
Risk assessments are key legal tools that can inform a number of legal decisions regarding parole sentencing and predict recidivism rates. Due to assessments being
historically performed by humans, they can be prone to bias and have come under various
amounts of scrutiny. The increased capability and application of machine learning
technology has lead the justice system to incorporate algorithms and codes to increase
accuracy and reliability. This study researched laypersons’ attitudes towards these algorithms
and how they would change when exposed to an algorithm that made errors in the risk
assessment process. Participants were tasked with reading two vignettes and answering a series
of questions to assess the differences in their perceptions towards machine learning and
clinician-based risk assessments. The research findings showed that individuals lent more trust
to clinicians and had more confidence in their assessments when compared to machines, but
were not significantly more punitive when it came to attributing blame and judgement for the
consequences of an incorrect risk assessment. Participants had a significantly more positive
attitude towards clinician-based risk assessments, noting their assessments as being more
reliable, informed, and trustworthy. Participants were also asked to come to a parole decision
using the assessment of either a clinician or machine learning algorithm at the end of the study
and rate their own confidence in their decision. Results found that participants were only
significantly less confident in their decision when exposed to previous instances of risk
assessments with error, but that there was no significant difference in their confidence based
solely on who conducted the assessment.
ContributorsMa, Angeline (Author) / Schweitzer, Nicholas (Thesis advisor) / Powell, Derek (Committee member) / Smalarz, Laura (Committee member) / Arizona State University (Publisher)
Created2023
Description
It has recently been argued that high-confidence eyewitness identifications are highly likely to be accurate regardless of the quality of viewing conditions experienced by the witness. However, new evidence suggests that evaluators of eyewitness identification evidence (e.g., jurors) do not trust highly confident eyewitnesses who experienced poor witnessing conditions. In fact, contextual information about poor witnessing conditions decreases evaluators’ belief of eyewitnesses to a greater extent for highly confident witnesses than for moderately confident witnesses. Why is the effect of witnessing-condition information greater for evaluations of high-confidence witnesses than for less confident witnesses? The current research tested the possibility that information about witnessing conditions influences evaluators’ perceptions of how well-calibrated a witness’s identification confidence is with the eyewitness’s accuracy. Using a paradigm adapted from the confidence calibration literature, I conducted an experiment to test this calibration account of the finding that witnessing condition information has a stronger effect on perceptions of highly confident witnesses than moderately confident witnesses. Although the results replicated the differential effects of witnessing condition context on perceptions of highly and moderately confident eyewitnesses, they failed to yield support for the confidence calibration hypothesis, potentially because the confidence calibration manipulation was ineffective. Directions for future research are discussed.
ContributorsLebensfeld, Taylor Cameron (Author) / Smalarz, Laura (Thesis advisor) / Salerno, Jessica (Committee member) / Arndorfer, Andrea (Committee member) / Arizona State University (Publisher)
Created2022
Description
Even though criminal justice outcomes frequently receive more media, public, and research attention than civil legal outcomes, civil legal outcomes are equally important in ensuring fairness, accountability, and justice for both individuals and society as a whole. This provides individuals an avenue to pursue justice and restitution for civil wrongs, protects civil rights, and compensates those who have been harmed financially. This study examined the relationship between regional implicit racial bias and racial disparities in outcomes of real-world civil trials. In particular, I explored whether the racial composition of the attorneys on the defense teams or race of the plaintiff predicted plaintiff verdicts and greater damage awards. I hypothesized that all-White defense attorney teams and plaintiffs would win their cases at higher rates and would subsequently be awarded more in damages than their non-White counterparts, especially in regions reporting high levels of implicit racial bias. Using real-world civil trials and Project Implicit Race IAT data, I conducted logistic and linear regression analyses to test the effects of race and regional bias on trial outcomes. The results showed that the likelihood of a pro-plaintiff verdict increased when the defense team included at least one non-White attorney. That is, more racially diverse defense teams won their cases less then all-White defense teams. Additionally, I found that the likelihood of a pro-plaintiff verdict decreased in regions reporting relatively higher levels of regional implicit racial bias. Future research aimed at understanding and reducing disparities and bias in the legal system should be extended to include civil trials and both attorney and client demographics.
ContributorsThomas, Sara (Author) / Salerno, Jessica (Thesis director) / Smalarz, Laura (Committee member) / Hall, Deborah (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor)
Created2023-05
Description
In the legal system, the prediction of a person’s risk of committing a crime has mostly been based on expert judgment. However, newer techniques that employ machine learning (ML)—a type of artificial intelligence—are being implemented throughout the justice system. Yet, there is a lack of research on how the public perceives and uses machine learning risk assessments in legal settings. In two mock-trial vignette studies, the perception of ML-based risk assessments versus more traditional methods was assessed. Study 1 was a 2 (severity of crime: low, high) x 2 (risk assessment type: expert, machine learning) x 2 (risk outcome: low, high) between-subjects design. Participants expressed ethical concerns and discouraged the use of machine learning risk assessments in sentencing decisions, but punishment recommendations were not affected. Study 2 was a within-subjects design where participants were randomly assigned read through one of three crime scenarios (violent, white-collar, sex offense) and one of three risk assessment techniques (expert, checklist, machine learning). Consistent with Study 1, participants had ethical concerns and disagreed with the use of machine learning risk assessments in bail decisions, yet their own decisions and recommendations did not reflect these concerns. Overall, laypeople express skepticism toward these new methods, but do not appear to differentially rely on ML-based versus traditional risk assessments in their own judgments.
ContributorsFine, Anna (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Committee member) / Smalarz, Laura (Committee member) / Arizona State University (Publisher)
Created2021
Description
Evaluators of eyewitness evidence (e.g., judges, jurors) often must determine whether an eyewitness’s identification of a police suspect is accurate or mistaken. It has recently been argued that a particular class of variables—suspect-bias variables—pose a unique threat to the reliability of eyewitness identification evidence. Unlike “general impairment” variables that generally impair eyewitness identification accuracy (e.g., poor viewing conditions, biased lineup instructions), suspect-bias variables produce a suspect-specific bias that increases the risk of confident misidentifications of innocent suspects. The goal of this research was to examine evaluators’ sensitivity to suspect-bias variables compared to general impairment variables, and to test whether sensitivity to suspect-bias differs as a function of whether the suspect-bias variable is under the control of the legal system (system suspect-bias) or outside of the legal system’s control (estimator suspect-bias). Participant-evaluators (N = 214) read eight crime vignettes paired with one of four different eyewitness variables (system suspect-bias, estimator suspect-bias, general impairment, or no-variable control) and rated the accuracy of each eyewitness. Evaluators also explained the reasoning for their accuracy rating, and their explanations were coded for mentions of procedural suggestion, eyewitness memory strength, memory contamination, and general eyewitness (un)reliability. Evaluators appear to be more sensitive to general impairment variables than to suspect-bias variables. This finding is alarming, as suspect-bias variables pose a greater threat to eyewitness reliability than general-impairment variables. Implications for the collection and evaluation of eyewitness evidence are discussed.
ContributorsKulak, Kylie (Author) / Smalarz, Laura (Thesis advisor) / Salerno, Jessica (Committee member) / Schweitzer, Nick (Committee member) / Arizona State University (Publisher)
Created2022
Description
The purpose of this thesis is to examine the available literature on suspect bias in the context of general impairment to evaluate support for the claim that general impairment exacerbates the effect of suspect bias to increase the risk of eyewitness misidentifications. To provide context for the importance of this issue, the misidentification of Ronald Cotton in the investigation of the assault of Jennifer Thompson-Cannino is discussed. I identified meta-analyses discussing the following suspect bias variables: prior mugshot exposure, exposure to composites, suspect-filler similarity, post-identification feedback, and the use of showups versus lineups to evaluate the literature. Using the articles citing the meta-analyses and cited within the meta-analyses, I found individual studies that manipulated suspect bias, in addition to a general impairment variable. Examples of general impairment variables include age, time delay, lineup presentation, lineup instructions, and options for identification responses. For each suspect-bias variable discussed, the literature provided inconsistent results. Based on the variations found in the data that was evaluated, I recommend that further examination of suspect-bias in the context of general impairment variables be conducted to garner a better, more cohesive understanding of the interaction between these variables.
ContributorsBarlow, Rosemary (Author) / Smalarz, Laura (Thesis director) / Arndorfer, Andrea (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor)
Created2022-05
Description
Eyewitness identification has been one of the most crucial components in identifying perpetrators in criminal cases. Misidentification of a suspect often has detrimental effects, with many innocent individuals being wrongfully convicted. In order to fully understand the causes of misidentification, a proper understanding of the process of eyewitness identification must be understood in order to ensure that fewer individuals are falsely imprisoned.
ContributorsKimmins, Emily (Author) / Ahmed, Maliha (Co-author) / DeCarolis, Claudine (Thesis director) / Robinson, Kevin (Committee member) / Smalarz, Laura (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / School of Politics and Global Studies (Contributor) / Department of Military Science (Contributor)
Created2022-05
Description
Since the advent of DNA analysis, organizations such as the Innocence Project have been able to exonerate people who were wrongfully convicted of crimes, often due to erroneous forensic evidence analysis. In many cases, analytical techniques, such as fingerprint analysis, toolmark analysis, or hair comparisons have been cited as nearly infallible sources of evidentiary fact. However, these methods rely on subjective interpretation by a forensic examiner and lack stringent, quantitative methods for ensuring reliability and accuracy. For most of these methods, the examiner is supplied only with the unknown sample from the crime scene, and a known sample from a suspect. This, combined with the influence of psychological factors such as confirmation bias, has resulted in the need for a reliable mechanism of ensuring the efficacy of a particular type of analysis as well as the objectivity, and competence of the analyst. One proposed method to resolve these issues is the use of a filler-control method, in which analysts are given an “evidence line-up” containing at least three samples: the unknown sample from the crime scene, a sample from the suspect, and at least one filler sample from an individual who is not involved in the investigation. This method provides a reliable method for estimating error rates for an analyst and can provide the analyst with direct feedback about their performance to accurately gauge their competence. This method also helps to prevent the introduction of confirmation bias, as the source of the samples is unknown to the analyst. The goal of the current research is to test the capacity of a filler-control method to lead to better confidence-calibration of examiners’ match judgements when compared to the conventional method. The hypothesis of this experiment is that participants using the filler control method will have improved performance and increased confidence calibration due to receiving feedback over the course of the trials when compared to participants using the traditional method.
ContributorsRocha, Bethany (Author) / Smalarz, Laura (Thesis director) / Kukucka, Jeff (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Natural Sciences (Contributor)
Created2022-05
Description
Civil juries are becoming an increasingly rare means of resolving civil disputes. One reason for this is widespread mistrust in jury decision-making do to highly publicized nuclear verdicts where verdicts do not seem to match the alleged harm suffered by a plaintiff. Critics allege that jurors are biased against defendants with deep pockets. This research aims to test whether there is evidence of so-called deep-pocket bias in juror decision-making. Previous research has compared how the wealth of defendants impacts jurors’ verdicts while other studies have compared how jurors’ verdicts are impacted when the defendant is an individual versus a corporation. The first aim is to explore the impact of defendant wealth and corporate identity on jurors’ liability verdicts and damage awards. The second aim is to explore whether the theory of dyadic morality helps to explain any potential observed deep-pocket biases. The study tested the hypothesis that perceptions of a defendant’s moral agency (in other words, their responsibility and intentionality) would predict jurors’ liability verdicts while perceptions of a defendant’s moral patiency (in other words, their vulnerability and capacity for suffering) would predict jurors’ damage awards. In a study of mock juror decision-making, results concluded that when assessing the same alleged wrongdoing and harm, jurors were more confident in a liable verdict against wealthy defendants and corporate defendants compared to poor defendants and individuals as defendants. Higher perceptions of a defendant’s moral agency did explain these effects. However, there was no evidence that defendant wealth or corporate identity influenced damage awards. Ultimately, in cases where plaintiffs portray themselves as a small and vulnerable “David” taking on a large and resourceful “Goliath,” juror decision-making on liability verdicts is likely to unfairly punish “Goliath” defendants, revealing deep-pocket biases against wealthy defendants and corporations.
ContributorsRosales, Breanna Olson (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Thesis advisor) / Smalarz, Laura (Committee member) / Arizona State University (Publisher)
Created2024
Description
Studies have repeatedly shown that mere exposure to ideas makes those ideas seem more true, a finding referred to as the “illusory truth” effect. This feature of cognition may heighten existing concerns surrounding the spread of misinformation. Recent studies have shown that the effect extends to fake news headlines and may increase the likelihood that someone shares misinformation. But is this evidence that mere exposure can affect our beliefs? The two leading accounts of the illusory truth effect argue that after initial exposure, participants sense a feeling of familiarity or “fluency” at test that they use as a sign the statement is true. Beliefs however, extend further than just truth ratings. Beliefs also guide actions and imply other beliefs. Three pre-registered experiments were conducted to examine whether mere exposure to statements induces genuine beliefs by first examining if participants draw implications from mere exposure in Study 1. Surprisingly, results indicated that exposure to “premise” statements affect participants’ truth ratings for novel “implied” statements, which cannot be explained by the familiarity or fluency accounts of the illusory truth effect. Study 2 replicated results from Study 1 and ruled out consistency pressure as an explanation for prior findings. Finally, Study 3 replicated results from Studies 1 and 2 and ensured they were not due to demand characteristics by conducting separate analysis for suspicious and non-suspicious participants. Since these findings cannot be explained by the predominant accounts of the illusory truth effect, the authors believe this is evidence of a new effect the “illusory implication” effect. More importantly, these findings suggest that the consequences of misinformation may be larger than previously thought and warrants further study into potential mechanisms driving the illusory implication effect.
ContributorsMikell, Justin (Author) / Powell, Derek (Thesis advisor) / Smalarz, Laura (Committee member) / Duran, Nicholas (Committee member) / Arizona State University (Publisher)
Created2024