Matching Items (12)
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- All Subjects: Law
- All Subjects: Betrayal
- Creators: Schweitzer, Nicholas
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
Several states within the United States have recently passed the Victim Life Photo Act, which allows prosecutors to present photographs of alleged murder victims when they were alive during the guilt phase of a trial. Critics argue that these photographs do not offer any relevant information about the crime or the defendant’s potential guilt and might bias jurors to vote guilty based on their sympathy for the victim—perhaps disproportionally so for high-status victims. Two mock trial experiments tested whether online participants who viewed alleged murder victim photographs would convict more because they increase anger, disgust, fear, sadness, and/or sympathy. Mock jurors who saw photographs of White (but not racial minority) victims while they were alive reported more sympathy for the victim relative to those who saw the same evidence without a photograph of the living victim—but the sympathy did not increase convictions (Study 1). Study 2 extended this study by testing whether the living victim photographs are more impactful in conjunction with seeing gruesome photographs of the victim after her death, creating a particularly disturbing contrast effect versus seeing the living photograph alone. Study 2 found that (a) living victim photographs on their own again had no effect on participants’ verdicts, (b) gruesome photographs on their own increased convictions through increased disgust, and (c) participants who saw both living and gruesome murder victim photographs (versus gruesome alone) were more conviction prone due to increased anger and sympathy. These studies inform current debates regarding the controversial Victim Life Photo Act: Admitting living victim photographs during the guilt phase—if presented along with gruesome photographs—can make jurors more sympathetic and angry, which can increase convictions.
ContributorsAdamoli, Madison Marie (Author) / Salerno, Jessica M (Thesis advisor) / Neal, Tess (Committee member) / Schweitzer, Nicholas (Committee member) / Arizona State University (Publisher)
Created2020