Critical Race Theory for HCI

By I. Ogbonnaya-Ogburu, Angela D. R. Smith, Alexandra To, Kentaro Toyama. International Conference on Human Factors in Computing Systems, 2020

412 citations recorded by the source corpus.

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.

Subfields: Education, Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/115c64ff235874c76da4f91dcac93c9d193babf3

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Citations in the corpus

“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability (2021, background): “First, as scholars contend that HCI must reckon with the racism, ableism, and transphobia we perpetuate both within and outside the field [55, 82, 103], we anticipate growing interventions toward justice.”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, background): “Ogbonnaya-Ogburu, Smith, To, and Toyama also suggest that in addition to these endeavours, researchers ought to be “other-conscious” [125]; in other words, to consider how their work will be viewed by people in other groups than their own, particularly racial minority groups.”
Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education (2021, background): “However, this is not a solution in educational contexts where our goal is to support learners by understanding their needs, especially when this results from systemic or racial disparity (Ogbonnaya-Ogburu et al. 2020).”
Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance (2021, background): “discuss the racism embedded within digital platforms and larger sociotechnical systems and call for critique and examination of racial bias of technology beyond just biased algorithms [58].”
Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas (2021, background): “Although the statistical efects related to race are not as highly statistically signifcant as some of the other efects, we felt it was critical to focus on Black participants’ experiences with content and account removals because addressing racial injustice and examining racial disparities in sociot”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, neutral): “Bardzell social computing design frameworks which have explicitly embraced specific politics and values [8, 24, 65, 81] Particularly with its focus on real-world applications, the artifacts produced by machine learning researchers and practitioners — for example, in the form of datasets — are…”