Surprising judgments about robot drivers: Experiments on rising expectations and blaming humans
Bibliographic record
Abstract
N-Reasons is an experimental Internet survey platform designed to enhance public participation in applied ethics and policy. N-Reasons encourages individuals to generate reasons to support their judgments, and groups to converge on a common set of reasons pro and con various issues. In the Robot Ethics Survey some of the reasons contributed surprising judgments about autonomous machines. Presented with a version of the trolley problem with an autonomous train as the agent, participants gave unexpected answers, revealing high expectations for the autonomous machine and shifting blame from the automated device to the humans in the scenario. Further experiments with a standard pair of human-only trolley problems refine these results. While showing the high expectations even when no autonomous machine is involved, human bystanders are only blamed in the machine case. A third experiment explicitly aimed at responsibility for driverless cars confirms our findings about shifting blame in the case of autonomous machine agents. We conclude methodologically that both results point to the power of an experimental survey based approach to public participation to explore surprising assumptions and judgments in applied ethics. However, both results also support using caution when interpreting survey results in ethics, demonstrating the importance of qualitative data to provide further context for evaluating judgments revealed by surveys. On the ethics side, the result about shifting blame to humans interacting with autonomous machines suggests caution about the unintended consequences of intuitive principles requiring human responsibility.
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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 | 0.013 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".