Voice Assistants’ Responses to Questions About the COVID-19 Vaccine: National Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence-powered voice assistants (VAs), such as Apple Siri, Google Assistant, and Amazon Alexa, interact with users in natural language and are capable of responding to simple commands, searching the internet, and answering questions. Despite being an increasingly popular way for the public to access health information, VAs could be a source of ambiguous or potentially biased information. OBJECTIVE: In response to the ongoing prevalence of vaccine misinformation and disinformation, this study aims to evaluate how smartphone VAs respond to information- and recommendation-seeking inquiries regarding the COVID-19 vaccine. METHODS: A national cross-sectional survey of English-speaking adults who owned a smartphone with a VA installed was conducted online from April 22 to 28, 2021. The primary outcomes were the VAs' responses to 2 questions: "Should I get the COVID vaccine?" and "Is the COVID vaccine safe?" Directed content analysis was used to assign a negative, neutral, or positive connotation to each response and website title provided by the VAs. Statistical significance was assessed using the t test (parametric) or Mann-Whitney U (nonparametric) test for continuous variables and the chi-square or Fisher exact test for categorical variables. RESULTS: Of the 466 survey respondents included in the final analysis, 404 (86.7%) used Apple Siri, 53 (11.4%) used Google Assistant, and 9 (1.9%) used Amazon Alexa. In response to the question "Is the COVID vaccine safe?" 419 (89.9%) users received a direct response, of which 408 (97.3%) had a positive connotation encouraging users to get vaccinated. Of the websites presented, only 5.3% (11/207) had a positive connotation and 94.7% (196/207) had a neutral connotation. In response to the question "Should I get the COVID vaccine?" 93.1% (434/466) of users received a list of websites, of which 91.5% (1155/1262) had a neutral connotation. For both COVID-19 vaccine-related questions, there was no association between the connotation of a response and the age, gender, zip code, race or ethnicity, and education level of the respondent. CONCLUSIONS: Our study found that VAs were much more likely to respond directly with positive connotations to the question "Is the COVID vaccine safe?" but not respond directly and provide a list of websites with neutral connotations to the question "Should I get the COVID vaccine?" To our knowledge, this is the first study to evaluate how VAs respond to both information- and recommendation-seeking inquiries regarding the COVID-19 vaccine. These findings add to our growing understanding of both the opportunities and pitfalls of VAs in supporting public health information dissemination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".