Yiqun T. Chen
Alexandra To
ABSTRACT
Engaging racially and ethnically diverse participants in Human-Computer Interaction (HCI) research is critical for creating safe, inclusive, and equitable technology. However, it remains unclear why and how HCI researchers collect study participants’ race and ethnicity. Through a systematic literature analysis of 2016–2021 CHI proceedings and a survey with 15 authors who published in these proceedings, we found that reporting race and ethnicity of participants is uncommon and that HCI researchers are far from consensus on the collection and analysis of this data. Because a majority (>90%) of the articles that report participants’ race and ethnicity are conducted in the United States, we focused our discussion on race and ethnicity accordingly. In future work, we plan to investigate considerations and best practices for collecting and analyzing race and ethnicity data in a global context.
- Julio Abascal and Colette Nicolle. 2005. Moving towards inclusive design guidelines for socially and ethically aware HCI. Interacting with Computers 17, 5 (Sept. 2005), 484–505.Google Scholar
Digital Library
- American Psychological Association. 2019. Publication Manual of the American Psychological Association: 7th Edition, 2020 Copyright (7ed.). American Psychological Association.Google Scholar
- American Sociological Association. 2017. The Importance of Collecting Data and Doing Social Science Research on Race. https://www.asanet.org/importance-collecting-data-and-doing-social-science-research-race. Accessed: 2021-8-5.Google Scholar
- Margo Anderson and Stephen E Fienberg. 2000. Race and ethnicity and the controversy over the US Census. Current Sociology 48, 3 (2000), 87–110.Google ScholarCross Ref
- Jack M Bloom. 2019. Class, Race, and the Civil Rights Movement, Second Edition. Indiana University Press.Google Scholar
- Center for Drug Evaluation and Research. 2019. Drug Trials Snapshots. Accessed: 2021-3-8.Google Scholar
- Derrick L Cogburn. 2003. HCI in the so-called developing world: what’s in it for everyone. Interactions 10, 2 (March 2003), 80–87.Google ScholarDigital Library
- Patricia Hill Collins and Sirma Bilge. 2020. Intersectionality. John Wiley & Sons.Google Scholar
- Nicholas David Bowman, Jihhsuan Tammy Lin, and Chieh Wu. 2021. A Chinese-Language Validation of the Video Game Demand Scale (VGDS-C): Measuring the Cognitive, Emotional, Physical, and Social Demands of Video Games. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–10.Google ScholarDigital Library
- Bryan Dosono and Bryan Semaan. 2019. Moderation Practices as Emotional Labor in Sustaining Online Communities: The Case of AAPI Identity Work on Reddit. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–13.Google ScholarDigital Library
- Susan M Dray, David A Siegel, and Paula Kotzé. 2003. Indra’s Net: HCI in the developing world. Interactions 10, 2 (March 2003), 28–37.Google ScholarDigital Library
- Tracy Frey and Roxanne K. Young. 2020. Race/Ethnicity. AMA Manual of Style. https://www.amamanualofstyle.com/view/10.1093/jama/9780190246556.001.0001/med-9780190246556-chapter-11-div2-23. Accessed: 2021-8-5.Google Scholar
- Darren Gergle and Desney S Tan. 2014. Experimental Research in HCI. In Ways of Knowing in HCI, Judith S Olson and Wendy A Kellogg (Eds.). Springer New York, New York, NY, 191–227.Google Scholar
- Maureen T Hallinan. 2001. Sociological Perspectives on Black-White Inequalities in American Schooling. Sociology of Education 74 (2001), 50–70.Google ScholarCross Ref
- Foad Hamidi, Lydia Stamato, Lisa Scheifele, Rian Ciela Visscher Hammond, and S Nisa Asgarali-Hoffman. 2021. “Turning the Invisible Visible”: Transdisciplinary Bioart Explorations in Human-DNA Interaction. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–15.Google ScholarDigital Library
- Julia Himmelsbach, Stephanie Schwarz, Cornelia Gerdenitsch, Beatrix Wais-Zechmann, Jan Bobeth, and Manfred Tscheligi. 2019. Do We Care About Diversity in Human Computer Interaction: A Comprehensive Content Analysis on Diversity Dimensions in Research. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–16.Google ScholarDigital Library
- Ariel T Holland and Latha P Palaniappan. 2012. Problems with the collection and interpretation of Asian-American health data: omission, aggregation, and extrapolation. Annals of Epidemiology 22, 6 (June 2012), 397–405.Google ScholarCross Ref
- Institute of Medicine (US) Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement. 2014. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. National Academies Press (US), Washington (DC).Google Scholar
- C P Jones. 2001. Invited commentary: “race,” racism, and the practice of epidemiology. American Journal of Epidemiology 154, 4 (Aug. 2001), 299–304; discussion 305–6.Google ScholarCross Ref
- Camara Phyllis Jones, Benedict I Truman, Laurie D Elam-Evans, Camille A Jones, Clara Y Jones, Ruth Jiles, Susan F Rumisha, and Geraldine S Perry. 2008. Using “socially assigned race” to probe white advantages in health status. Ethnicity & Disease 18, 4 (2008), 496–504.Google Scholar
- Bliss Kaneshiro, Olga Geling, Kapuaola Gellert, and Lynnae Millar. 2011. The challenges of collecting data on race and ethnicity in a diverse, multiethnic state. Hawaii medical journal 70, 8 (Aug. 2011), 168–171.Google Scholar
- J S Kaufman and R S Cooper. 2001. Commentary: considerations for use of racial/ethnic classification in etiologic research. American Journal of Epidemiology 154, 4 (Aug. 2001), 291–298.Google ScholarCross Ref
- Masoud Mehrabi Koushki, Borke Obada-Obieh, Jun Ho Huh, and Konstantin Beznosov. 2021. On Smartphone Users’ Difficulty with Understanding Implicit Authentication. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–14.Google ScholarDigital Library
- Nancy Krieger, Jaquelyn L Jahn, and Pamela D Waterman. 2017. Jim Crow and estrogen-receptor-negative breast cancer: US-born black and white non-Hispanic women, 1992-2012. Cancer Causes & Control 28, 1 (Jan. 2017), 49–59.Google ScholarCross Ref
- Sharon M Lee. 1993. Racial classifications in the US census: 1890–1990. Ethnic and racial studies 16, 1 (Jan. 1993), 75–94.Google Scholar
- Kevin E Levay, Jeremy Freese, and James N Druckman. 2016. The Demographic and Political Composition of Mechanical Turk Samples. SAGE Open 6, 1 (Jan. 2016).Google ScholarCross Ref
- Sarah Lopez, Yi Yang, Kevin Beltran, Soo Jung Kim, Jennifer Cruz Hernandez, Chelsy Simran, Bingkun Yang, and Beste F Yuksel. 2019. Investigating Implicit Gender Bias and Embodiment of White Males in Virtual Reality with Full Body Visuomotor Synchrony. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–12.Google ScholarDigital Library
- Neda Maghbouleh, Ariela Schachter, and René D Flores. 2022. Middle Eastern and North African Americans may not be perceived, nor perceive themselves, to be White. PNAS 119, 7 (Feb. 2022).Google ScholarCross Ref
- Patrick L Mason and Andrew Matella. 2014. Stigmatization and racial selection after September 11, 2001: self-identity among Arab and Islamic Americans. IZA Journal of Migration 3, 1 (Oct. 2014), 1–21.Google ScholarCross Ref
- National Institutes of Health. 2001. NOT-OD-01-053: NIH POLICY ON REPORTING RACE AND ETHNICITY DATA: SUBJECTS IN CLINICAL RESEARCH. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-01-053.html. Accessed: 2021-8-5.Google Scholar
- National Science Foundation. 2017. Technical Notes. https://www.nsf.gov/statistics/2017/nsf17310/technical-notes.cfm. Accessed: 2021-8-5.Google Scholar
- Anna Offenwanger, Alan John Milligan, Minsuk Chang, Julia Bullard, and Dongwook Yoon. 2021. Diagnosing Bias in the Gender Representation of HCI Research Participants: How it Happens and Where We Are. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21, Article 399). Association for Computing Machinery, New York, NY, USA, 1–18.Google ScholarDigital Library
- Ihudiya Finda Ogbonnaya-Ogburu, Angela D R Smith, Alexandra To, and Kentaro Toyama. 2020. Critical Race Theory for HCI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–16.Google ScholarDigital Library
- Newton G Osborne and Marvin D Feit. 1992. The Use of Race in Medical Research. JAMA: the journal of the American Medical Association 267, 2 (Jan. 1992), 275–279.Google ScholarCross Ref
- Cale J Passmore, Max V Birk, and Regan L Mandryk. 2018. The Privilege of Immersion: Racial and Ethnic Experiences, Perceptions, and Beliefs in Digital Gaming. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–19.Google ScholarDigital Library
- Pew Research Center. 2015. Multiracial in America: Proud, Diverse and Growing in Numbers. https://www.pewsocialtrends.org/wp-content/uploads/sites/3/2015/06/2015-06-11_multiracial-in-america_final-updated.pdf. Accessed: 2021-6-25.Google Scholar
- Race in HCI Collective, Angela D R Smith, Adriana Alvarado Garcia, Ian Arawjo, Audrey Bennett, Khalia Braswell, Bryan Dosono, Ron Eglash, Denae Ford, Daniel Gardner, Shamika Goddard, Jaye Nias, Cale Passmore, Yolanda Rankin, Naba Rizvi, Carol F Scott, Jakita Thomas, Alexandra To, Ihudiya Finda Ogbonnaya-Ogburu, and Marisol Wong-Villacres. 2021. Keepin’ it real about race in HCI. Interactions 28, 5 (Aug. 2021), 28–33.Google Scholar
- Shan M Randhawa, Tallal Ahmad, Jay Chen, and Agha Ali Raza. 2021. Karamad: A Voice-based Crowdsourcing Platform for Underserved Populations. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–15.Google ScholarDigital Library
- Yolanda A Rankin and Jakita O Thomas. 2019. Straighten up and fly right: Rethinking intersectionality in HCI research. Interactions 26, 6 (2019), 64–68.Google ScholarDigital Library
- Helen Hatab Samhan. 2001. Who Are Arab Americans?Google Scholar
- Morgan Klaus Scheuerman, Katta Spiel, Oliver L. Haimson, Foad Hamidi, and Stacy M. Branham. 2021. HCI Gender Guidelines. https://www.morgan-klaus.com/gender-guidelines.html. Accessed: 2021-8-6.Google Scholar
- Ari Schlesinger, W Keith Edwards, and Rebecca E Grinter. 2017. Intersectional HCI: Engaging Identity through Gender, Race, and Class. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 5412–5427.Google ScholarDigital Library
- Jonathan Schwabish and Alice Feng. 2020. Applying Racial Equity Awareness in Data Visualization. (Aug. 2020).Google Scholar
- Angela D R Smith, Alex A Ahmed, Adriana Alvarado Garcia, Bryan Dosono, Ihudiya Ogbonnaya-Ogburu, Yolanda Rankin, Alexandra To, and Kentaro Toyama. 2020. What’s Race Got To Do With It? Engaging in Race in HCI. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–8.Google ScholarDigital Library
- Katta Spiel, Oliver L Haimson, and Danielle Lottridge. 2019. How to do better with gender on surveys: a guide for HCI researchers. Interactions 26, 4 (June 2019), 62–65.Google ScholarDigital Library
- Simone Stumpf, Anicia Peters, Shaowen Bardzell, Margaret Burnett, Daniela Busse, Jessica Cauchard, and Elizabeth Churchill. 2020. Gender-Inclusive HCI Research and Design: A Conceptual Review. Now Foundations and Trends.Google Scholar
- Christian Sturm, Alice Oh, Sebastian Linxen, Jose Abdelnour Nocera, Susan Dray, and Katharina Reinecke. 2015. How WEIRD is HCI? Extending HCI Principles to other Countries and Cultures. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI EA ’15). Association for Computing Machinery, New York, NY, USA, 2425–2428.Google ScholarDigital Library
- Kentaro Toyama. 2010. Human–Computer Interaction and Global Development. Foundations and Trends® in Human–Computer Interaction 4, 1(2010), 1–79.Google Scholar
- US Census Bureau. 2019. National Demographic Analysis Tables: 2020. Accessed: 2021-3-8.Google Scholar
- Tavish Vaidya, Daniel Votipka, Michelle L Mazurek, and Micah Sherr. 2019. Does Being Verified Make You More Credible? Account Verification’s Effect on Tweet Credibility. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–13.Google Scholar
- Judy van Biljon and Karen Renaud. 2019. Human-Computer Interaction for Development (HCI4D): The Southern African Landscape. In Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D. Springer International Publishing, 253–266.Google Scholar
- Kelly Walters, Dimitri A Christakis, and Davene R Wright. 2018. Are Mechanical Turk worker samples representative of health status and health behaviors in the U.S.?PloS one 13, 6 (June 2018), e0198835.Google Scholar
- Kim M Williams. 2006. Mark One or More. University of Michigan Press.Google Scholar
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- Collecting and Reporting Race and Ethnicity Data in HCI
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