Ethical decision making behind the wheel – A driving simulator study
Bibliographic record
Abstract
Over the past several years, there has been considerable debate surrounding ethical decision making in situations resulting in inevitable casualties. Given enough time and all other things being equal, studies show that drivers will typically decide to strike the fewest number of pedestrians in scenarios where there is a choice between striking several versus one or no pedestrians. However, it is unclear whether drivers behave similarly under situations of time pressure. In our experiment in a driving simulator, 32 drivers were given up to 2 s to decide which group of pedestrians to avoid among groups of larger (5) or smaller (≤1) number of pedestrians. Our findings suggest that while people frequently choose utilitarian decisions in the typical, abstract manifestations of the Trolley Problems, drivers can fail to make utilitarian decisions in simulated driving environments under a restricted period of time representative of the time they would have to make the same decision in the real world (2 s). Analysis of eye movement data shows that drivers are less likely to glance at left and right sides of crosswalks under situations of time duress. Our results raise critical engineering and ethical questions. From a cognitive engineering standpoint, we need to know how long at minimum a driver needs to make simple, moral decisions in different scenarios. From an ethical standpoint, we may need to evaluate whether automated vehicle algorithms can aid decision making on our behalf when there is not enough time for a driver to make a moral decision.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".