Improving the Public's Perception of Autonomous Vehicles by Communicating the Consistency of Autonomous Vehicle Algorithms
Bibliographic record
Abstract
Despite autonomous vehicles (AVs) being safer than human drivers, people are averse to their presence on roads. Across three studies (N = 4,014), we examined peoples’ perceptions of human drivers and AVs acting within a moral dilemma. Scenarios involved an out-of-control vehicle (piloted by a human, or autonomously) that could stay on its present course and hit five pedestrians, or swerve and hit a single stranger. Participants were given a description of the pilot’s final action and then judged them on several dimensions (e.g., blame, acceptability, predictability). We find evidence of AV aversion across all studies, with participants judging AVs more negatively (e.g., more blameworthy) than human drivers despite performing identical actions. Additionally, Studies 1 and 2 presented some participants with a statement outlining the consistency of AV algorithms, which increased perceived predictability and reduced AV aversion in some cases. In Study 3, some participants were given scenarios in which control of the vehicle was transferred prior to a pilot’s actions. Participants’ were averse to this transfer, as both AVs and human drivers were perceived as less predictable and judged more negatively after taking control of the vehicle. Overall, our findings highlight peoples’ aversion to autonomous and semi-autonomous vehicles, while also demonstrating that messages highlighting the consistency of AV algorithms have the potential to improve perceptions and thus reduce barriers for their eventual mass adoption.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".