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Description
For decades, researchers have found that jurors are consistently unable - or unwilling - to disregard inadmissible evidence when instructed to do so by a judge. The legal system ignores the problem entirely: judges have repeatedly affirmed that a judge’s instructions to disregard are a sufficient safeguard of defendants’ constitutional rights, regardless of whether the jury actually disregards the inadmissible evidence. This study tested four interventions derived from psychological research to identify the combination that most effectively helped jurors disregard inadmissible evidence (operationalized by lower conviction rates). It was hypothesized that the most effective interventions identified in Study 1 would yield significantly lower conviction rates when compared to traditional instructions to disregard in Study 2. The interventions were tested in Study 1 using Multiphase Optimization Strategy (MOST) methodology to identify the optimized intervention package through regression analysis. Study 2 served as a randomized controlled trial in which treatment as usual (a judge’s instructions to disregard) was compared to the optimized intervention package. Participants were randomly assigned to either (1) be exposed to no inadmissible evidence, (2) receive inadmissible evidence and treatment as usual, (3) receive inadmissible evidence, treatment as usual and the optimized intervention package, or (4) receive the inadmissible evidence without objection. Logistic regression revealed that jurors who were given an instruction to disregard produced lower conviction rates when they also received the optimized intervention package. Interpretation, limitations, and calls to action are discussed.
ContributorsSandberg, Pamela Nicole (Author) / O'Hara, Karey L. (Thesis advisor) / Neal, Tess M.S. (Committee member) / Hall, Deborah L. (Committee member) / Arizona State University (Publisher)
Created2023
Description
When questioning children during courtroom testimony, attorneys are instructed to use questions that are short and simple to address children’s cognitive abilities; however, this typically leads to anaphora. Anaphora occurs when a word is substituted for a previously mentioned word, phrase, or concept. For example, the pronoun “he” in “Bill is moving to New York. He is very excited.” indicates an anaphora since the word “he” replaces the name Bill. When asked a question that includes a pronoun-specific anaphora, the respondent must use cognitive skills to refer back to the initial referent. This likely means that as the number of conversational turns between the initial referent and the end of the reference increases, there will be more probable miscommunications between children and attorneys in cases of alleged Child Sexual Abuse (CSA). In this thesis, I analyzed 40 testimonies from cases of alleged child sexual abuse (5-10 years old, 90% female), located attorney use of pronoun anaphora, backward reference distances, and identified probable misunderstandings. I identified 137 probable misunderstandings within 2,940 question-answer pairs that included pronoun anaphora. Attorneys averaged 4.1 questions before clarifying the referent (SD = 10.14), sometimes extending up to 146 lines, leading to considerable backwards referencing. The distance between the anaphora and referent had a significant effect on misunderstandings, where each additional Q-A pair made misunderstandings more likely to occur. To reduce misunderstanding, attorneys should avoid pronoun anaphora of excessive length that require children to backward reference.
ContributorsRuiz-Earle, Ciara Aisling (Author) / Stolzenberg, Stacia (Thesis advisor) / Fine, Adam (Committee member) / Yan, Shi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Due to the internet being in its infancy, there is no consensus regarding policy approaches that various countries have taken. These policies range from strict government control to liberal access to the internet which makes protecting individual private data difficult. There are too many loopholes and various forms of policy on how to approach protecting data. There must be effort by both the individual, government, and private entities by using theoretical mixed methods to approach protecting oneself properly online.
ContributorsPeralta, Christina A (Author) / Scheall, Scott (Thesis advisor) / Hollinger, Keith (Thesis advisor) / Alozie, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The rise in popularity of applications and services that charge for access to proprietary trained models has led to increased interest in the robustness of these models and the security of the environments in which inference is conducted. State-of-the-art attacks extract models and generate adversarial examples by inferring relationships between a model’s input and output. Popular variants of these attacks have been shown to be deterred by countermeasures that poison predicted class distributions and mask class boundary gradients. Neural networks are also vulnerable to timing side-channel attacks. This work builds on top of Subneural, an attack framework that uses floating point timing side channels to extract neural structures. Novel applications of addition timing side channels are introduced, allowing the signs and arrangements of leaked parameters to be discerned more efficiently. Addition timing is also used to leak network biases, making the framework applicable to a wider range of targets. The enhanced framework is shown to be effective against models protected by prediction poisoning and gradient masking adversarial countermeasures and to be competitive with adaptive black box adversarial attacks against stateful defenses. Mitigations necessary to protect against floating-point timing side-channel attacks are also presented.
ContributorsVipat, Gaurav (Author) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2023
Description
The honors thesis explores the relationship between academic majors and the entrepreneurial mindset, focusing on whether Computer Science students have a perceived edge. The study involves a survey of 121 diverse Arizona State University students, complemented by in-depth interviews with 12 participants across various majors. Insights reveal the complex interplay of psychological factors influencing major selection, with Computer Science students advocating for more entrepreneurship-related courses. Challenges include time constraints for STEM majors in pursuing extracurricular activities and a call for universities to proactively integrate entrepreneurship education. While acknowledging the study's limitations, the thesis emphasizes the need for universities to adapt to changing student mindsets. Despite hurdles, the collective belief is that internal determination and effort drive students forward.
ContributorsAgarwal, Sarthak (Author) / Meuth, Ryan (Thesis director) / Sebold, Brent (Committee member) / Barrett, The Honors College (Contributor)
Created2023-12
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to select an elected official that accurately represents their interests. Instead, gerrymandering has legislators to choose their voters.
In recent years, the Supreme Court has heard challenges to state legislature-drawn districts, most recently in Allen v. Milligan for Alabama and Moore v. Harper for North Carolina. The highest court of the United States ruled that the two state maps were gerrymandered, and in coming to their decision, the 9 justices relied on a plethora of amicus briefs- one of which included the Markov Chain Monte Carlo method, a computational method used to find gerrymandering.
Because of how widespread gerrymandering has become on both sides of the political aisle, states have moved to create independent redistricting commissions. Qualitative research regarding the efficacy of independent commissions is present, but there is little research using the quantitative computational methods from these SCOTUS cases. As a result, my thesis will use the Markov Chain Monte Carlo method to answer if impartial redistricting commissions (like we have in Arizona) actually preclude unfair redistricting practices.
My completed project is located here:
https://dheetideliwala.github.io/honors-thesis/
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
Description
Children have been known to engage in socially curious behaviors, such as frequently asking questions about other people’s feelings and actions (Friedman et al., 2018). Social curiosity helps children engage in cultural learning and understanding the explicit and implicit rules of society (Hartung & Renner, 2013). However, little is known about how social curiosity may impact children’s moral development. Seeking out social information may help form connections between children, increasing the extent to which they behave prosocially to others. Additionally, similar constructs to social curiosity (theory of mind and empathy) are linked to prosocial behavior (Imuta et al., 2016; Ding & Lu, 2016). The present study therefore investigates the relationship between social curiosity and prosocial sharing. To test the hypothesis that children who are primed to be socially curious will exhibit increased prosocial sharing, we used the Social Uncertainty Paradigm to elicit social curiosity in children who then completed a sticker sharing task. Our hypothesis was not supported; no significant differences between the sharing behaviors of children primed for social curiosity and those who were not. Additional research is needed to conclude whether social curiosity may be linked to prosocial behavior in a way that this study was not able to determine.
ContributorsTrimble, Gemma (Author) / Lucca, Kelsey (Thesis director) / Lee, Nayen (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-12