Bibliographic record
Abstract
The possibility of autonomous-machine-caused harm generates doctrinal and theoretical challenges for assigning tort liability. With emergent capabilities, autonomous machines disrupt the structure of interpersonal rights and duties in tort law, framed by conditions of foreseeability and proximate causation. Where algorithmic processes are unintelligible, self-modifying, and unpredictable, the concern goes, algorithmic harms will be untraceable to tortious human agency. As a result, their costs will simply lie where they fall—on faultless victims. This outcome would be unfair and objectionable: A failure of tort’s mechanisms of corrective justice means faultless victims would disproportionately bear the accident costs of autonomous machines. This article suggests that the doctrinal form of vicarious liability is a promising strategy to ground tort liability for autonomous-machine-caused harm. Human or corporate deployers should be held liable for tortious harm caused by autonomous machines in the course of deployment. In this account, autonomous machines constitute a novel legal category as pure legal agents without legal personhood. In reconceiving vicarious liability—and the legal classification of autonomous machines—the article seeks to promote commonsensical liability outcomes for autonomous-machine-caused harm, consistent with tort’s doctrinal and theoretical structure of corrective justice.
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.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".