Analysis of factors that may be essential in the decision to fly on fully autonomous passenger airliners
Bibliographic record
Abstract
Summary This research investigates factors that influence opinion in the decision to fly on fully autonomous passenger airliners primarily from the perspective of aviation and technology professionals. Bayesian statistical inference and a two‐level fractional factorial survey are used to sample passengers' views on fully autonomous airliners. Eight trust, safety, and cost factors are incorporated into a vignette set in the future. Factors include automation levels, safety records, liability guarantees, airline integrity, and service disruptions. Dependent variables exist in five post‐vignette questions and essentially ask “Would you” or “Would you not” be willing to fly on a fully autonomous airliner? Sixteen versions of the vignette, each with unique trust, safety, and cost levels, present varying (unknown) degrees of influence to the survey respondents. For every demographic, the research shows a 99% statistically significant difference between the “prior” and “posterior” sampled population proportions willing to fly. The most significant positive influence involves integrity characteristics of the airline, while the most negative influence relates to life insurance liability guarantees. Research from 2003 suggested that this mode of travel would be acceptable to only 10.5% of respondents. When the 2003 research is used as a Bayesian prior probability, the resulting posterior probability for the demographics sampled can be modeled as a beta distribution, indicating 95% probability that the sampled proportion of the population willing to fly is between 33.2% and 36.4%. After adjusting for age and profession demographics to match the US population, the 95% probability bounds on the proportion willing to fly are 31.35% and 34.15%. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".