Blame It on the Self-Driving Car: How Autonomous Vehicles Can Alter Consumer Morality
Bibliographic record
Abstract
Abstract Autonomous vehicles (AVs) are expected to soon replace human drivers and promise substantial benefits to society. Yet, consumers remain skeptical about handing over control to an AV. Partly because there is uncertainty about the appropriate moral norms for such vehicles (e.g., should AVs protect the passenger or the pedestrian if harm is unavoidable?). Building on recent work on AV morality, the current research examined how people resolve the dilemma between protecting self versus a pedestrian, and what they expect an AV to do in a similar situation. Five studies revealed that participants considered harm to a pedestrian more permissible with an AV as compared to self as the decision agent in a regular car. This shift in moral judgments was driven by the attribution of responsibility to the AV and was observed for both severe and moderate harm, and when harm was real or imagined. However, the effect was attenuated when five pedestrians or a child could be harmed. These findings suggest that AVs can change prevailing moral norms and promote an increased self-interest among consumers. This has relevance for the design and policy issues related to AVs. It also highlights the moral implications of autonomous agents replacing human decision-makers.
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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 | 0.002 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".