Collecting and Reporting Race and Ethnicity Data in HCI

Chen, Yiqun T.; Smith, Angela D. R.; Reinecke, Katharina; To, Alexandra

doi:10.1145/3491101.3519685

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…Yet empirical work in HCI-viewed across the field as a whole-is disparate; how we report is varied and sometimes spotty. This is certainly not a novel criticism; we echo calls from other researchers who are critically reflecting on our research reporting practices, e.g., with regards to the reporting of race and ethnicity data [14], brain signal experiment data [68], participant compensation data [65], inter-rater reliability in qualitative research [57], specific measures [71] and questionnaires [43,47], engagement with self-determination theory [81], artifact descriptions [33], and inferential statistics [12], to list just a few. These issues may arise in part due to page limits or efforts to ensure paper length matches perceived contribution, but may also be due to lack of community-driven standardization and education.…”

Section: Charting a New Trajectory: Critical Issues And Provocationsmentioning

confidence: 85%

The Systematic Review-lution: A Manifesto to Promote Rigour and Inclusivity in Research Synthesis

Rogers

Seaborn

2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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Section: Charting a New Trajectory: Critical Issues And Provocationsmentioning

confidence: 85%

The Systematic Review-lution: A Manifesto to Promote Rigour and Inclusivity in Research Synthesis

Rogers

Seaborn

2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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“…We must also consider the intersections when it comes to matters of social identity [11,26,37,40]. While work on social identities in HCI and adjacent spaces is growing-in populations [8,21,40] and in design materials, such as personas [26], creations [42], and underlying technologies [7]-we must go beyond analyzing multiple factors in isolation by considering how these factors intersect in ways that may be difficult or impossible to tease out. When it comes to kawaii, we may specifically expect a link between perceptions of younger age categories and femininity.…”

Section: Operationalizing Kawaii As a Sociocultural Phenomenonmentioning

confidence: 99%

Can Voice Assistants Sound Cute? Towards a Model of Kawaii Vocalics

Seaborn

Nam

Keckeis

et al. 2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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The Japanese notion of "kawaii" or expressions of cuteness, vulnerability, and/or charm is a global cultural export. Work has explored kawaii-ness as a design feature and factor of user experience in the visual appearance, nonverbal behaviour, and sound of robots and virtual characters. In this initial work, we consider whether voices can be kawaii by exploring the vocal qualities of voice assistant speech, i.e., kawaii vocalics. Drawing from an age-inclusive model of kawaii, we ran a user perceptions study on the kawaii-ness of younger-and older-sounding Japanese computer voices. We found that kawaii-ness intersected with perceptions of gender and age, i.e., gender ambiguous and girlish, as well as VA features, i.e., fluency and artificiality. We propose an initial model of kawaii vocalics to be validated through the identification and study of vocal qualities, cognitive appraisals, behavioural responses, and affective reports.

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“…However, to ensure consistency and accuracy, it is recommended that we then code these identities to pre-existing categories or labels. By adopting these methods, IP effectively accounts for the nuances of race [19]. IP utilizes a matching process that pairs a crowdworker's racial background with a corresponding facial verification task.…”

Section: Ip's Human Componentmentioning

confidence: 99%

“…We then coded these identities into the labels used by the RFW dataset, which included the labels: "Black or African American", "Asian", "Caucasian", and "Indian. " This approach is important in large-scale data collection efforts [27], and helps to effectively account for the nuances of race [19]. Additionally, we only selected crowdworkers who had no prior experience with facial verification tasks to control for confounding factors.…”

Section: Participants (Crowdworkers)mentioning

confidence: 99%

Inclusive Portraits: Race-Aware Human-in-the-Loop Technology

Flores-Saviaga,

Curtis,

Savage

2023

Equity and Access in Algorithms, Mechanisms, and Optimization

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Fig. (1) Overview of Inclusive Portait's functionality.AI has revolutionized the processing of various services, including the automatic facial verification of people. Automated approaches have demonstrated their speed and efficiency in verifying a large volume of faces, but they can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of "human-in-the-loop" (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers' individual characteristics and backgrounds. This paper proposes a new approach, called Inclusive Portraits (IP), that connects with social theories around race to design a racially-aware human-in-the-loop system. Our experiments have provided evidence that incorporating race into human-in-the-loop (HITL) systems for facial verification can significantly enhance performance, especially for services delivered to people of color. Our findings also highlight the importance of considering individual worker characteristics in the design of HITL systems, rather than treating workers as a homogenous group. Our research has significant design implications for developing AI-enhanced services that are more inclusive and equitable.

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Collecting and Reporting Race and Ethnicity Data in HCI

Cited by 8 publications

References 29 publications

The Systematic Review-lution: A Manifesto to Promote Rigour and Inclusivity in Research Synthesis

The Systematic Review-lution: A Manifesto to Promote Rigour and Inclusivity in Research Synthesis

Can Voice Assistants Sound Cute? Towards a Model of Kawaii Vocalics

Inclusive Portraits: Race-Aware Human-in-the-Loop Technology

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