“It’s Kind of Like Code-Switching”: Black Older Adults’ Experiences with a Voice Assistant for Health Information Seeking
By Christina N. Harrington, Radhika Garg, Amanda T. Woodward, Dimitri Williams. 2021
123 citations recorded by the source corpus.
Black older adults from lower socioeconomic environments are often neglected in health technology interventions. Voice assistants have a potential to make healthcare more accessible to older adults, yet, little is known about their experiences with this type of health information seeking, especially Black older adults. Through a three-phase exploratory study, we explored health information seeking with 30 Black older adults in lower-income environments to understand how they ask health-related questions, and their perceptions of the Google Home being used for that purpose. Through our analysis, we identified the health information needs and common search topics, and discussed the communication breakdowns and types of repair performed. We contribute an understanding of cultural code-switching that has to be done by these older adults when interacting with voice assistants, and the importance of such phenomenon when designing for historically excluded groups.
Subfields: Artificial Intelligence, Demography, Information Systems, Human-Computer Interaction
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In Artificial Intelligence
- Gavin Abercrombie
- Su Lin Blodgett
- Alexandra Sasha Luccioni
- Irene Solaiman
- Amanda Cercas Curry
- Zeerak Talat
- Dylan Baker
- Yejin Choi
In Demography
In Information Systems
- Irene Solaiman
- Gavin Abercrombie
- Marie-Therese Png
- Zeerak Talat
- Yacine Jernite
- Alexandra Sasha Luccioni
- Jesse Dodge
- Su Lin Blodgett
In Human-Computer Interaction
- Gavin Abercrombie
- Marie-Therese Png
- Yacine Jernite
- Su Lin Blodgett
- Amanda Cercas Curry
- Margaret Mitchell
- Sara Hooker
Citations in the corpus
- Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “For example, automatic speech recognition models (ASR), which convert spoken language (audio) to text have been shown to exhibit racial disparities [130], forcing people to adapt to engage with such systems [100] and has implications (see 4.”
- Mirages: On Anthropomorphism in Dialogue Systems (2023, background): “As a result, people code switch when engaging with dialogue systems in order to have successful interactions (Foster and Stuart-Smith, 2023; Harrington et al., 2022).”