The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Critical Race Theory for HCI
Abstract
The human-computer interaction community has made some efforts toward racial diversity, but the outcomes remain meager. We introduce critical race theory and adapt it for HCI to lay a theoretical basis for race-conscious efforts, both in research and within our community. Building on the theory's original tenets, we argue that racism is pervasive in everyday socio-technical systems; that the HCI community is prone to "interest convergence", where concessions to inclusion require benefits to those in power; and that the neoliberal underpinnings of the technology industry itself propagate racism. Critical race theory uses storytelling as a means to upend deep-seated assumptions, and we relate several personal stories to highlight ongoing problems of race in HCI. The implications: all HCI research must be attuned to issues of race; participation of underrepresented minorities must be sought in all of our activities; and as a community, we cannot become comfortable while racial disparities exist.
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Citations
Cites
Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color
Crenshaw, K. (1991) · Stanford Law Review · doi:10.2307/1229039 · Cited by 32,792
The weirdest people in the world?
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0999152x · Cited by 12,641
Critical Race Methodology: Counter-Storytelling as an Analytical Framework for Education Research
Solórzano, D. G., & Yosso, T. J. (2002) · Qualitative Inquiry · doi:10.1177/107780040200800103 · Cited by 4,257 · cited 3×
“Work with communities of color – while desirable – is particularly fraught, as there is a tendency to assume deficit narratives, in which communities of color are cast as lacking something that can be supplied with technology-based interventions [112].”
BackgroundAlgorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709
Brown v. Board of Education and the Interest-Convergence Dilemma
Bell, D. A. (1980) · Harvard Law Review · doi:10.2307/1340546 · Cited by 2,574
Speculative Everything: Design, Fiction, and Social Dreaming
(2014) · Choice Reviews Online · doi:10.5860/choice.51-5390 · Cited by 1,952
Storytelling for Oppositionists and Others: A Plea for Narrative
Delgado, R. (1989) · Michigan Law Review · doi:10.2307/1289308 · Cited by 1,939 · cited 1×
“• There is a uniqueness to the voice of color [33], and storytelling is a means for it to be heard [112].”
BackgroundImplicit and explicit prejudice and interracial interaction.
Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.82.1.62 · Cited by 1,567
Dark Matters: On the Surveillance of Blackness
Browne, S. (2015) · doi:10.1215/9780822375302 · Cited by 1,135
Risk of being killed by police use of force in the United States by age, race–ethnicity, and sex
Edwards, F., Lee, H., & Esposito, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821204116 · Cited by 916
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Pendse, S. R., Nkemelu, D., Bidwell, N. J., Jadhav, S., Pathare, S., De Choudhury, M., & Kumar, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501982 · Cited by 115
Winkle, K., McMillan, D., Arnelid, M., Harrison, K., Balaam, M., Johnson, E., & Leite, I. (2023) · Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3568162.3576973 · Cited by 111
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Metadata
- Authors
- Ogbonnaya-Ogburu, I. F., Smith, A. D. R., To, A., & Toyama, K.
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-16
- OpenAlex
- W3029464893
- Cited by
- 396
- References
- 18
- FWCI
- 60.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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