Born to Fear the Machine? Genetic and Environmental Influences on Negative Attitudes toward AI Agents
Bibliographic record
Abstract
Despite the rapid development of artificial intelligence (AI) agents, substantial individual differences in public acceptance persist. To explain the difference in attitudes toward AI agents, existing research has primarily focused on environmental factors. However, evolutionary psychology research suggests that the mechanism of outgroup rejection has a genetic basis, highlighting the need to explore the potential genetic underpinnings of negative attitudes toward AI agents as an outgroup in human society. This study examines the genetic basis of negative attitudes toward AI agents and their relationship with related personality traits, using a twin study design to assess negative attitudes toward AI agents, victim sensitivity, and moral preferences. Univariate genetic analyses revealed significant heritability of these negative attitudes. Bivariate analyses further identify shared genetic influences between victim sensitivity and personal-level fear and wariness toward robots. Similarly, a shared genetic basis is observed between the moral preferences concerning authority and sociotechnical blindness anxiety toward AI agents. These findings extend the understanding of social cognition in AI agents by emphasizing the role of genetic factors in shaping attitudes toward them. Moreover, they provide new insights for enhancing public acceptance of AI agents and optimizing human-machine interactions.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".