Bibliographic record
Abstract
Throughout the world, artificial intelligence (AI) technology has become an integral part of everyday life and work. The emergence of intelligent media has resulted in significant changes to the news industry, largely due to the implementation of AI news anchors. The purpose of this study is to examine new audiences' perceptions of AI news anchors. A content analysis was conducted to determine how news audiences perceive AI news anchors. Comments posted on YouTube and Facebook videos that show AI news anchors reporting the news were analyzed. It was observed that AI news anchors have varying effects on their news audiences since they were first implemented in China in 2018. Findings show that 65% of all posted comments were negative, whereas 34% were positive. The results of this study were contradicting at times. For instance, many people consider AI news anchors to be fake because of their unrealistic movements, whereas others believe they resemble human newscasters in appearance. Furthermore, some viewers expressed concern that AI news anchors may be utilized by governments to promote propaganda or negative political messages. Moreover, findings indicate that news audiences are increasingly concerned that AI will result in the loss of jobs for real news anchors, deterring people from entering journalism or reporting professions.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".