More Like this

1. Understanding the Experiences that Contribute to the Inclusion of Underrepresented Groups in Computing

   https://doi.org/10.1109/FIE44824.2020.9274113  

   Kargarmoakhar, Maral; Lunn, Stephanie; Zahedi, Leila; Ross, Monique; Hazari, Zahra; Weiss, Mark Allen; Georgiopoulos, Michael; Christensen, Ken; Solis, Tiana (October 2020, 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   The lack of diversity in computing fields in the United States is a known issue. Students enter the computing fields with the intention of graduating; however, a large number leave and do not persist after enrolling, due to discrimination and biases. This particularly concerns groups already underrepresented in computing fields, such as women, Black/African American students, and Hispanic/Latinx students. However, there are various experiences that can make students feel more included or excluded in the field. Some of these experiences include internships, undergraduate research, capstone courses, and projects, etc. Drawing on Astin's I-E-O model and applying a random forest algorithm, we measure the feature importance of 14 distinct experiences on 1650 students' feelings of inclusivity in the computing field. We observe that there are gender and racial differences in terms of the opinions of computing fields' inclusivity. For example, tutoring experience, job offers, and job experience are considered some of the most important factors for female's perceived inclusiveness of women. However, men perceived women's inclusivity differently, based on the experiences they engaged in. We also looked at the perceived inclusiveness of computing fields for ethnically and racially underrepresented groups, such as Hispanic/Latinx students. Understanding the effect of different experiences on students of both genders with different races and ethnicities on the perceived inclusion could assist the computing community to provide more cohesive experiences that benefits all students and helps them to feel more welcome. 

   more » « less
2. Understanding the Experiences that Contribute to the Inclusion of Underrepresented Groups in Computing

   Maral Kargarmoakhar, Stephanie Lunn (October 2020, 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   The lack of diversity in computing fields in the United States is a known issue. Students enter the computing fields with the intention of graduating; however, a large number leave and do not persist after enrolling, due to discrimination and biases. This particularly concerns groups already underrepresented in computing fields, such as women, Black/African American students, and Hispanic/Latinx students. However, there are various experiences that can make students feel more included or excluded in the field. Some of these experiences include internships, undergraduate research, capstone courses, and projects, etc. Drawing on Astin's I-E-O model and applying a random forest algorithm, we measure the feature importance of 14 distinct experiences on 1650 students' feelings of inclusivity in the computing field. We observe that there are gender and racial differences in terms of the opinions of computing fields' inclusivity. For example, tutoring experience, job offers, and job experience are considered some of the most important factors for female's perceived inclusiveness of women. However, men perceived women's inclusivity differently, based on the experiences they engaged in. We also looked at the perceived inclusiveness of computing fields for ethnically and racially underrepresented groups, such as Hispanic/Latinx students. Understanding the effect of different experiences on students of both genders with different races and ethnicities on the perceived inclusion could assist the computing community to provide more cohesive experiences that benefits all students and helps them to feel more welcome. 

   more » « less
3. Understanding the Experiences that Contribute to the Inclusion of Underrepresented Groups in Computing

   Kargarmoakhar, M. (October 2020, IEEE Frontiers in Education)

   null (Ed.)

   The lack of diversity in computing fields in the United States is a known issue. Students enter the computing fields with the intention of graduating; however, a large number leave and do not persist after enrolling, due to discrimination and biases. This particularly concerns groups already underrepresented in computing fields, such as women, Black/African American students, and Hispanic/Latinx students. However, there are various experiences that can make students feel more included or excluded in the field. Some of these experiences include internships, undergraduate research, capstone courses, and projects, etc. Drawing on Astin’s I-E-O model and applying a random forest algorithm, we measure the feature importance of 14 distinct experiences on 1650 students’ feelings of inclusivity in the computing field. We observe that there are gender and racial differences in terms of the opinions of computing fields’ inclusivity. For example, tutoring experience, job offers, and job experience are considered some of the most important factors for female’s perceived inclusiveness of women. However, men perceived women’s inclusivity differently, based on the experiences they engaged in. We also looked at the perceived inclusiveness of computing fields for ethnically and racially underrepresented groups, such as Hispanic/Latinx students. Understanding the effect of different experiences on students of both genders with different races and ethnicities on the perceived inclusion could assist the computing community to provide more cohesive experiences that benefits all students and helps them to feel more welcome. 

   more » « less
4. Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles

   https://doi.org/10.1145/3663740  

   Anderson, Andrew; Noa_Guevara, Jimena; Moussaoui, Fatima; Li, Tianyi; Vorvoreanu, Mihaela; Burnett, Margaret (May 2024, ACM Transactions on Interactive Intelligent Systems)

   Motivations:Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive? Objectives:To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age. Methods:We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages. Results & Implications:Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles. 

   more » « less
5. A Socio-ecological Imperative for Broadening Participation in Coastal and Estuarine Research and Management

   https://doi.org/10.1007/s12237-021-00944-z  

   Harris, L. A.; Grayson, T.; Neckles, H. A.; Emrich, C. T.; Lewis, K. A.; Grimes, K. W.; Williamson, S.; Garza, C.; Whitcraft, C. R.; Pollack, J. Beseres; et al (May 2021, Estuaries and Coasts)

   Abstract For most of the scientific disciplines associated with coastal and estuarine research, workforce representation does not match the demographics of communities we serve, especially for Black, Hispanic or Latino, and Indigenous peoples. This essay provides an overview of this inequity and identifies how a scientific society can catalyze representational, structural, and interactional diversity to achieve greater inclusion. Needed changes go beyond representational diversity and require an intentional commitment to build capacity through inclusivity and community engagement by supporting anti-racist policies and actions. We want to realize a sense of belonging on the part of scientists in society at large and enable research pursuits through a lens of social justice in service of coastal communities. Minimally, this framework offers an avenue for increased recruitment of individuals from more diverse racial and ethnic identities. More broadly, the mechanisms described here aim to create a culture in scientific societies in which social justice, driven by anti-racist actions, produces systemic change in how members of scientific societies approach, discuss, and address issues of inequity. We have written this essay for members of the coastal and marine science community who are interested in change. We aim to call in new voices, allies, and champions to this work. 

   more » « less