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1. Engineering Allies: The Personalities of Cisgender Engineering Students

   Rohde, J.A.; Kirn, A.; Godwin, A. (June 2017, ASEE Annual Conference & Exposition)

   https://peer.asee.org/28248 The research draws from a larger study conducted at four large public universities examining the non-normative attitudes of first-year engineering students and how these attitudes might affect their collegiate experience and the development of their engineering identity. Within the survey demographics section, students were asked to report their gender with as many options as they felt appropriate to describe themselves. Students were given the option to respond “male,” “female,” “cisgender,” “transgender,” “agender,” “genderqueer,” and/or “a gender not listed.” Of the students surveyed, 2,697 identified themselves as male or female. Of this population, 55 students additionally identified themselves as cisgender. A Welch’s t-test revealed that factors relating to engineering identity were significantly different between cisgender students who self-identified and those who did not. Self-identified cisgender students possessed higher scores on factors measuring components of engineering identity, such as Physics Performance/Competence beliefs (p = 0.001, Cohen’s d = 0.412). These students were also rated as higher on Openness from the “Big 5” personality measures (p = 0.006, Cohen’s d = 0.403), and scored significantly lower on Conscientiousness from the “Big 5” personality measures (p = 0.028, Cohen’s d = 0.343). These data highlight the differences between cisgender identified and non-identified students. Higher Openness results indicate that cisgender students are significantly more attentive of individuals’ inner feelings and may seek out more variety in their experiences than their non-cis-identified peers. Lower Conscientiousness scores reveal that cisgender students, on average, are less likely to conform to traditional cultural norms. Additionally, stronger scores relating to engineering identity indicate that cisgender-identified students feel that they belong in engineering. Together, these findings suggest that cisgender students possess traits and attitudes that could position them as ambassadors to or changemakers within engineering culture. Future research will work to understand these differences qualitatively to inform ways in which these individuals may serve as allies or “bridgers” for individuals within engineering who do not conform to gender and sexual orientation binaries. 

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2. The Nuanced Nature of Trust and Privacy Control Adoption in the Context of Google

   https://doi.org/10.1145/3544548.3581387  

   Ul Haque, Ehsan; Khan, Mohammad Maifi; Fahim, Md Abdullah (April 2023, CHI Conference on Human Factors in Computing Systems (CHI ‘23))

   This paper investigates how trust towards service providers and the adoption of privacy controls belonging to two specific purposes (control over “sharing” vs. “usage” of data) vary based on users’ technical literacy. Towards that, we chose Google as the context and conducted an online survey across 209 Google users. Our results suggest that integrity and benevolence perceptions toward Google are significantly lower among technical participants than non-technical participants. While trust perceptions differ between non-technical adopters and non-adopters of privacy controls, no such difference is found among the technical counterparts. Notably, among the non-technical participants, the direction of trust affecting privacy control adoption is observed to be reversed based on the purpose of the controls. Using qualitative analysis, we extract trust-enhancing and dampening factors contributing to users’ trusting beliefs towards Google’s protection of user privacy. The implications of our findings for the design and promotion of privacy controls are discussed in the paper. 

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3. Is There any Evidence of Historical Changes in Gender Differences in American High School Students’ Math Competence-Related Beliefs from the 1980s to the 2010s?

   Rubach, C.; Lee, G.; Starr, C.; Gao, Y.; Safavian, N.; Dicke A.-L.; Eccles, J. S.; & Simpkins, S. (January 2022, International journal of gender science and technology)

   In this replication study, we examined gender differences in students’ math competence-related beliefs from 9th to 12th grade and tested gender differences within four racial/ethnic groups. In order to test the potential historical changes in these patterns and to counteract the replication crisis in psychology, this study employed six U.S. datasets collected from the 1980s to 2010s. Using a total sample of 24,290 students (49.5% male students; 11% African-, 9% Asian-; 30% Latinx- and 50% European-Americans), we found gender differences in students’ math competence-related beliefs favoring boys at all grade levels. By comparing effect sizes across datasets, we found no evidence that these gender differences varied by dataset or by historical time. The results across race/ethnicity with a subsample of 23,070 students indicated meaningful gender differences in students’ math competence-related beliefs favoring boys at all grade levels among Asian-, European-, and Latinx-Americans, but not among African-Americans where differences favored girls in 12th grade. Overall, our findings provide no evidence of historical changes concerning gender differences in students’ math competence-related beliefs across datasets. Our findings illustrate the importance of replicating empirical findings across datasets and using an intersectional lens to investigate math motivation. 

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4. Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination

   https://doi.org/10.1609/hcomp.v12i1.31602  

   Kingsley, Sara; Zhi, Jiayin; Deng, Wesley Hanwen; Lee, Jaimie; Zhang, Sizhe; Eslami, Motahhare; Holstein, Kenneth; Hong, Jason I; Li, Tianshi; Shen, Hong (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   There has been growing recognition of the crucial role users, especially those from marginalized groups, play in uncovering harmful algorithmic biases. However, it remains unclear how users’ identities and experiences might impact their rating of harmful biases. We present an online experiment (N=2,197) examining these factors: demographics, discrimination experiences, and social and technical knowledge. Participants were shown examples of image search results, including ones that previous literature has identified as biased against marginalized racial, gender, or sexual orientation groups. We found participants from marginalized gender or sexual orientation groups were more likely to rate the examples as more severely harmful. Belonging to marginalized races did not have a similar pattern. Additional factors affecting users’ ratings included discrimination experiences, and having friends or family belonging to marginalized demographics. A qualitative analysis offers insights into users' bias recognition, and why they see biases the way they do. We provide guidance for designing future methods to support effective user-driven auditing. 

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5. Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation

   https://doi.org/10.1145/3422841.3423533  

   Joo, Jungseock; Kärkkäinen, Kimmo (October 2020, FATE/MM '20: Proceedings of the 2nd International Workshop on Fairness, Accountability, Transparency and Ethics in Multimedia)

   null (Ed.)

   Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact. We use these synthesized images to measure counterfactual fairness of commercial computer vision classifiers by examining the degree to which these classifiers are affected by gender and racial cues controlled in the images, e.g., feminine faces may elicit higher scores for the concept of nurse and lower scores for STEM-related concepts. 

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