ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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The Influence of Error on Perceptions of Machine Learning vs. Clinician-Based Risk Assessments

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…

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
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Punishing Goliath: Biased Juror Decision-Making Against Wealthy Defendants and Corporations

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…

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