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1. Exploring Transgender and Gender Nonconforming Engineering Undergraduate Experiences through Autoethnography

   Haverkamp, A. ; Butler, A. ; Pelzl, N. S. ; Bothwell, M. K. ; Montfort, D. ; Driskill, Q. ( April 2019 , 2019 CoNECD - The Collaborative Network for Engineering and Computing Diversity)

   Undergraduate programs in engineering are demanding, time consuming, and inherently social endeavors for young adults. Strong social support networks and communities which foster success are frequently found to increase student retention and perseverance through their engineering degree programs. Students with marginalized identities in higher education are met with additional workloads – managing their social identity, negotiating stereotypes, and finding belonging. Existing research shows that a student’s experience in in higher education is particularly shaped by gender interactions. This has been shown to be particularly true in engineering, whose gender demographics and professional culture is described as hegemonically masculine. Research onmore »gender in engineering has typically framed gender within a rigid, essentialized cisgender binary. Current literature is lacking detail on the processes used by gender diverse students in the transgender and gender nonconforming (TGNC) community as they navigate the gendered engineering field. We wish to highlight the experiences that undergraduate engineering students have had in relation to their social support and perceptions of gender as it relates to engineering culture within their undergraduate programs. Two students participated in autoethnography as a method of data collection to meet this objective. Collaborative autoethnographic methods position the students as coauthors and coresearchers to ensure the validity of analysis alongside the project’s primary investigators. Using a resiliency framework and critical autoethnographic analysis, the primary focus is on the ways these students have formed support systems and their perception of the social landscape in engineering. Through exploring how students persevere through their programs we may uncover points of intervention to strengthen these support systems.« less
2. Diagnosing Gender Bias in Image Recognition Systems

   https://doi.org/10.1177/2378023120967171  

   Schwemmer, Carsten ; Knight, Carly ; Bello-Pardo, Emily D. ; Oklobdzija, Stan ; Schoonvelde, Martijn ; Lockhart, Jeffrey W. ( January 2020 , Socius: Sociological Research for a Dynamic World)

   Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three timesmore »more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.« less
3. Cultural stereotypes and sense of belonging contribute to gender gaps in STEM.

   Master, Allison ; Meltzoff, Andrew N. ( January 2020 , International journal of gender science and technology)

   There is a need to help more students succeed in science, technology, engineering, and mathematics (STEM) education, with particular interest in reducing current gender gaps in motivation and participation. We propose a new theoretical model, the STEreotypes, Motivation, and Outcomes (STEMO) developmental model, to account for and integrate recent data emerging in social and developmental psychology. Based on this model, we synthesize research suggesting that social factors, such as stereotypes and self-representations about “belonging,” are powerful contributors to observed gender differences in STEM interest and academic outcomes. The review has four parts. First, we examine how cultural stereotypes specific tomore »STEM contribute to gender gaps by negatively impacting interest and academic outcomes. Second, we review the central role of the self-representations affected by those stereotypes, including the particular importance of a sense of belonging. Third, we discuss various interventions that buffer against stereotypes and enhance a sense of belonging to reduce gender gaps in STEM interest and academic outcomes. Finally, we suggest theory-driven directions for future research. By organizing the research in this way, our review and theoretical analysis clarify key factors contributing to current gender gaps in STEM and mechanisms by which psychological interventions can help address STEM gender gaps.« less
4. What might books be teaching young children about gender?

   https://doi.org/10.31234/osf.io/ntgfe  

   Lewis, M. ; Borkenhagen, M.C. ; Converse, E. ; Lupyan, G. ; Seidenberg, M.S. ( January 2022 , Psychological science)

   We investigated how gender is represented in children’s books using a novel 200,000 word corpus comprising 247 popular, contemporary books for young children. Using human judgments and word co-occurrence data, we quantified gender biases of words in individual books and in the whole corpus. We find that children’s books contain many words that adults judge as gendered. Semantic analyses based on co-occurrence data yielded word clusters related to gender stereotypes (e.g., feminine: emotions; masculine: tools). Co-occurrence data also indicate that many books instantiate gender stereotypes identified in other research (e.g., girls are better at reading and boys at math). Finally,more »we used large-scale data to estimate the gender distribution of the audience for individual books, and find that children are more often exposed to gender stereotypes for their own gender. Together the data suggest that children’s books may be an early source of gender associations and stereotypes.« less
5. Software fairness

   https://doi.org/10.1145/3236024.3264838  

   Brun, Yuriy ; Meliou, Alexandra ( January 2018 , Proceedings of the New Ideas and Emerging Results Track at the 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE))

   A goal of software engineering research is advancing software quality and the success of the software engineering process. However, while recent studies have demonstrated a new kind of defect in software related to its ability to operate in fair and unbiased manner, software engineering has not yet wholeheartedly tackled these new kinds of defects, thus leaving software vulnerable. This paper outlines a vision for how software engineering research can help reduce fairness defects and represents a call to action by the software engineering research community to reify that vision. Modern software is riddled with examples of biased behavior, from automatedmore »translation injecting gender stereotypes, to vision systems failing to see faces of certain races, to the US criminal justice system relying on biased computational assessments of crime recidivism. While systems may learn bias from biased data, bias can also emerge from ambiguous or incomplete requirement specification, poor design, implementation bugs, and unintended component interactions. We argue that software fairness is analogous to software quality, and that numerous software engineering challenges in the areas of requirements, specification, design, testing, and verification need to be tackled to solve this problem.« less