Blame It on the Self-Driving Car: How Autonomous Vehicles Can Alter Consumer Morality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it