The Thorny Challenge of Making Moral Machines: Ethical Dilemmas with Self-Driving Cars
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 The algorithms that control AVs will need to embed moral principles guiding their decisions in situations of unavoidable harm. Manufacturers and regulators are confronted with three potentially incompatible objectives: being consistent, not causing public outrage, and not discouraging buyers. The presented moral machine study is a step towards solving this problem as it tries to learn how people all over the world feel about the alternative decisions the AI of self-driving vehicles might have to make. The global study displayed broad agreement across regions regarding how to handle unavoidable accidents. To master the moral challenges, all stakeholders should embrace the topic of machine ethics: this is a unique opportunity to decide as a community what we believe to be right or wrong, and to make sure that machines, unlike humans, unerringly follow the agreed-upon moral preferences. The integration of autonomous cars will require a new social contract that provides clear guidelines about who is responsible for different kinds of accidents, how monitoring and enforcement will be performed, and how trust among all stakeholders can be engendered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.006 |
| 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 | 0.000 |
| 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