Cultural Variation in Attitudes Towards Social Chatbots
Bibliographic record
Abstract
Across two studies (Total N = 1,659), we found evidence for cultural differences in attitudes towards socially bonding with conversational AI. In Study 1 (N = 675), university students with an East Asian cultural background expected to enjoy a conversation with a chatbot (vs. with a human) more than students with European background. Moreover, they were less uncomfortable and more approving of someone else socially connecting with a chatbot (vs. human) than the students with a European background. In Study 2 (pre-registered; N = 984), we found similar evidence for cultural differences comparing samples of Chinese and Japanese adults currently living in East Asia to adults currently living in the United States. Critically, these cultural differences were explained by East Asian participants increased propensity to anthropomorphize technology. Overall, our findings suggest there is cultural variability in attitudes towards chatbots and that these differences are mediated by differences in anthropomorphism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".