Matching Items (29)
Description
American media contributes significantly to popular culture, societal values, and perceptions not only within the United States but also globally. As society has evolved, representation in media has become increasingly important. Unfortunately, misinformation and biases remain widespread and have led to biases and prejudice against certain groups such as Muslims.
Thus, this thesis delves into Muslim misrepresentation in American cinema since the events of September 11, 2001. Through a comprehensive content analysis of several films via "The Riz Test" and previous studies, this thesis aims to uncover patterns and themes in the narrative and address questions about how portrayals of Muslims have changed over time and how contemporary films attempt to disengage from stereotypes.
This paper shows that films released after 2017 have offered a more favorable view of Muslims, but there is still a lot of work to be done in order to ensure nuanced and complex portrayals.
ContributorsHashmi, Iqra (Author) / Mousa, Neimeh (Thesis director) / Sulayman, Umar (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-12
Description
Generative Adversarial Networks are designed, in theory, to replicate the distribution of the data they are trained on. With real-world limitations, such as finite network capacity and training set size, they inevitably suffer a yet unavoidable technical failure: mode collapse. GAN-generated data is not nearly as diverse as the real-world data the network is trained on; this work shows that this effect is especially drastic when the training data is highly non-uniform. Specifically, GANs learn to exacerbate the social biases which exist in the training set along sensitive axes such as gender and race. In an age where many datasets are curated from web and social media data (which are almost never balanced), this has dangerous implications for downstream tasks using GAN-generated synthetic data, such as data augmentation for classification. This thesis presents an empirical demonstration of this phenomenon and illustrates its real-world ramifications. It starts by showing that when asked to sample images from an illustrative dataset of engineering faculty headshots from 47 U.S. universities, unfortunately skewed toward white males, a DCGAN’s generator “imagines” faces with light skin colors and masculine features. In addition, this work verifies that the generated distribution diverges more from the real-world distribution when the training data is non-uniform than when it is uniform. This work also shows that a conditional variant of GAN is not immune to exacerbating sensitive social biases. Finally, this work contributes a preliminary case study on Snapchat’s explosively popular GAN-enabled “My Twin” selfie lens, which consistently lightens the skin tone for women of color in an attempt to make faces more feminine. The results and discussion of the study are meant to caution machine learning practitioners who may unsuspectingly increase the biases in their applications.
ContributorsJain, Niharika (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Manikonda, Lydia (Committee member) / Arizona State University (Publisher)
Created2020
Description
Biases have been studied in many legal contexts, including sexual assault cases. Sexual assault cases are complex because there are many stages that biases can come into play and have lasting effects on the rest of the case proceedings. One aspect that has not been widely explored is how people perceive institutions’ liability in sexual assault cases based on an obligation to create non-discriminating environments for members and employees according to laws like Title VII and Title IX. The current project focused on how and why cognitive biases affect laypeople’s judgment. Specifically, laypeople’s ability to discern the strength of evidence in civil sexual assault cases against institutions. This was addressed in a series of two studies, with samples collected from Prolific Academic (n = 90) and Arizona State University students (n = 188) for Study 1 (N = 278), and Prolific Academic in Study 2 (N = 449). Both studies used Latin-square design methods, with within and between subject elements, looking at how confirmation bias influenced decisions about whether an institution demonstrated negligence, and thus liability, in the way they responded to sexual assault allegations within their institution. Results from these studies suggest that jurors are overall accurately able to differentiate between weak and strong cases. However, consistent with previous literature, jurors may be susceptible to confirmation bias from outside information (e.g., news stories) and negatively influenced by their personal attitudes (e.g., rape myth acceptance). Given the increased attention of the Me Too movement, these results provide an initial insight into how individuals may be judging these types of cases against institutions.
ContributorsMcCowan, Kristen (Author) / Neal, Tess M.S. (Thesis advisor) / Salerno, Jessica M (Committee member) / Davis, Kelly C (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cognitive heuristics, or mental shortcuts, sometimes give rise to biases that can influence decision making. These biases may be particularly impactful in a legal context where decision making has lifelong consequences. One such legal decision falls upon social workers who are often tasked with providing custodial recommendations in child custody cases. Across a series of 2 studies, I explored the role of confirmation bias in social worker decision making, the potential value of blinding to reduce bias, as well as social workers’ perceptions of their own biases. Social workers were given detailed case materials describing a custody case between the state and a father. Participants were randomly assigned to read a previous examiner’s positive evaluation of a father, a negative evaluation of the father, or were blinded to a previous examiners rating. Social workers engaged in confirmation bias, such that those who read a positive evaluation of the father viewed him more positively than participants who read a negative evaluation of the father, despite the fact that all of the actual case evidence remained constant. Blinding did not appear to mitigate the bias. In study 2, social workers viewed themselves as less biased than their peers and less biased than other experts in a different field – signifying the presence of a bias blindspot. Together, my findings suggest the need to further explore how bias might affect judgments and also how to mitigate biases, such as making experts aware of their potential for bias.
ContributorsDenne, Emily (Author) / Neal, Tess M.S. (Thesis advisor) / Stolzenberg, Stacia N. (Committee member) / Fabricius, William (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Culture is a living, dynamic concept that influences the lives of all human beings, making it one of the cornerstone building blocks of the human experience. However, there is a widespread assumption that culture matters more for some people than others. Recent studies have found evidence of a cultural (mis)attribution bias among psychologists, the tendency to exaggerate the role of cultural factors in the behavior of racial/ethnic minorities while simultaneously exaggerating the role of personal psychological factors in the behavior of the racial/ethnic majority (Causadias, Vitriol, & Atkins, 2018a; 2018b). This study aims to explore the cultural (mis)attribution bias, and how it manifests in the beliefs and attitudes of undergraduate students at ASU. Additionally, this paper will also explore the implications of those results and how to apply that knowledge to our daily interactions with the people around us.
ContributorsKwon, Woochan (Author) / Causadias, José (Thesis director) / Pedram, Christina (Committee member) / Korous, Kevin (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Psychology (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Individuals are often susceptible to bias in their given fields; however, they may not acknowledge nor be aware of this phenomenon. Moreover, people typically can recognize bias in others yet fail to realize that they themselves are susceptible to their own bias. This is referred to as the bias blind spot, an unconscious meta-cognitive bias. Unconscious bias can lead to impaired decisions and can cause problems in the field, especially if professionals are defensive about bias mitigation procedures if they see them as unnecessary and threatening. The purpose of this thesis is to analyze and examine the perceptions that professional forensic psychologists have about bias in themselves and bias in their colleagues. Eighty-four professional forensic psychologists were surveyed and asked about their perception of bias in themselves, their colleagues, an average adult, and experts in another domain: forensic science. For this study, these forensic psychologists were asked to predict the bias that they themselves might have in their judgment, that forensic scientists might have in their judgment, and that the average adult would have. As hypothesized, and consistent with the bias blind spot, professional forensic psychologists rated their peers in the same field as having a higher amount of bias in their decisions than they themselves. Moreover, they also rated other professionals in similar fields (forensic science) as having a higher bias rate than themselves. In addition, participants rated bias mitigating procedures as being a higher threat to their field than a different domain (i.e., forensic science) – consistent with hypotheses. These results suggest that professional forensic psychologists are susceptible to the bias blind spot and its consequences.
Keywords: implicit bias, bias blind spot, perceptions, judgment, mitigating procedures
Keywords: implicit bias, bias blind spot, perceptions, judgment, mitigating procedures
ContributorsVelazquez, Annelisse Danielle (Author) / Neal, Tess (Thesis director) / Salerno, Jessica (Committee member) / School of Social and Behavioral Sciences (Contributor) / School of Social Transformation (Contributor) / School of Criminology and Criminal Justice (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In the US, underrepresented racial and ethnic minorities receive less than adequate health care in comparison to White Americans. This is attributed to multiple factors, including the long history of structural racism in the US and in the medical field in particular. A factor that is still prevalent today is the lack of diversity within the healthcare workforce. Racial and ethnic minorities are underrepresented in most healthcare occupations. Moreover, many physicians may continue to harbor implicit biases that may interfere with giving adequate care to patients of different backgrounds. We propose that diversity in healthcare should be increased through educational programs and a revamp of existing systems such as medical schools. The increased diversity would mitigate some of the health disparities that exist amongst minorities, as medical professionals are more likely to give adequate care to those who are members of the same community. Increased diversity would also help to increase the cultural competency of physicians as a whole.
ContributorsWebb, Linden (Author) / Lopez, Adriana (Co-author) / Martin, Thomas (Thesis director) / Feagan, Mathieu (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description
Street-level bureaucracy (SLB) theory argues that public servants take shortcuts when making decisions about the delivery of public services. These shortcuts can lead SLBs to treat citizens unfairly. Public administration and political science researchers have found some evidence that street-level bureaucrats act in biased ways towards ethnic and racial minorities, citizens of lower socioeconomic status, and religious minorities. I expand on the SLB literature on discrimination by examining whether SLBs discriminate based on the political ideology of citizens. According to the Ideological-Conflict Hypothesis, individuals act in biased ways towards others whose political values conflict with their own. Using the Ideological-Conflict Hypothesis, I test whether SLBs working in local governments discriminate against citizens based on political ideology and whether discrimination is related to type of service delivery (e.g. needs based versus universal). I carry out two audit experiments to test for discrimination. One audit experiment tests for political ideology discrimination in a need-based program among a sample of public housing authorities in the United States (US). The sample is limited to areas where over 60% of citizens voted for the Democratic candidate in the 2020 Presidential Election (n = 274)—and where over 60% voted for the Republican candidate (n = 274). The other audit experiment tests for political ideology discrimination in the delivery of a universal service using a sample municipal parks departments in US cities. The sample is cities with over 25,000 residents where at least 60% of citizens in the county voted for the Democratic candidate in the 2020 Presidential Election (n = 227) and counties where at least 60% of citizens voted for Republican candidate (n = 227). The treatment signals that an email is from a conservative citizen, a liberal citizen, or a citizen with no identifiable political ideology. The results of my dissertation provide some support for the Ideological-Conflict Hypothesis and evidence indicates SLBs discriminate based on political ideology. The results do not find differences in political discrimination for needs-based public service delivery compared to universal public service delivery.
ContributorsOlsen, Jared (Author) / Feeney, Mary K. (Thesis advisor) / Favero, Nathan (Committee member) / Miller, Susan M. (Committee member) / Stritch, Justin (Committee member) / Arizona State University (Publisher)
Created2023