Bibliographic record
Abstract
Tort law allows parties to seek remedies (typically in the form of monetary damages) for losses caused by a wrongdoer’s intentional conduct, failure to exercise reasonable care, and/or introduction of a specific risk into society. The scope of tort law makes it especially relevant for individuals who are harmed as a result of an artificial intelligence (AI)-system operated by another person, company, or government agent with whom the injured person has no pre-existing legal relationship (e.g. no contract or commercial relationship). This chapter examines the application of three primary areas of tort law to AI-systems. Plaintiffs might pursue intentional tort actions when an AI-system is used to intentionally carry out harmful conduct. While this is not likely to be the main source of litigation, intentional torts can provide remedies for harms that might not be available through other areas of law. Negligence and strict liability claims are likely to be more common legal mechanisms in the AI context. A plaintiff might have a more straightforward case in a strict liability claim against a wrongdoer, but these claims are only available in specific situations in Canada. A negligence claim will be the likely mechanism for most plaintiffs suffering losses from a defendant’s use of an AI-system. Negligence actions for AI-related injuries will present a number of complexities and challenges for plaintiffs. Even seemingly straightforward preliminary issues like identifying who to name as a defendant might raise barriers to accessing remedies through tort law. These challenges, and some potential opportunities, are outlined below.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".